Methods of detection of cancer using peptide profiles

ABSTRACT

The disclosed methods address the identification and monitoring of cancer in a subject using serum peptide profiles. Such profiles allow the detection of the differential presence of certain serum peptide markers in comparison with controls. The profiles can be determined employing mass spectrometry.

CROSS-REFERENCE TO RELATED APPLICATIONS/PATENTS & INCORPORATION BY REFERENCE

This application claims priority to U.S. Provisional Application Ser. No. 60/708,676, filed Aug. 16, 2005, the contents of which are incorporated herein by reference.

STATEMENT OF GOVERNMENTAL SUPPORT

This work was funded by NIH grant nos. 1 R21CA1119425, 5 P30 CA08748 and 5 P50 CA 92629. The government may have certain rights to this invention.

Each of the applications and patents cited in this text, as well as each document or reference cited in each of the applications and patents (including during the prosecution of each issued patent; “application cited documents”), and each of the PCT and foreign applications or patents corresponding to and/or paragraphing priority from any of these applications and patents, and each of the documents cited or referenced in each of the application cited documents, are hereby expressly incorporated herein by reference. More generally, documents or references are cited in this text, either in a Reference List before the paragraphs, or in the text itself; and, each of these documents or references (“herein-cited references”), as well as each document or reference cited in each of the herein-cited references (including any manufacturer's specifications, instructions, etc.), is hereby expressly incorporated herein by reference.

BACKGROUND OF THE INVENTION

Serum biomarkers are used for diagnosis of disease and for predicting and monitoring response to treatment (Sidransky, D. 2002. Nat Rev Cancer 2:210-219; Bidart, J. M., et al. 1999. Clin Chem 45:1695-1707). Most clinically useful markers, to date, have been plasma proteins that require individual immunoassays for quantitation (Jortani, S. A., et al. 2004. Clin Chem 50:265-278; Watts, N. B. 1999. Clin Chem 45:1359-1368). Human serum also contains smaller peptides that constitute an entity known as the serum ‘peptidome’. Advances in mass spectrometry (MS) now permit the display of hundreds of small to medium sized peptides from microliter volumes of serum (Koomen, J. M., et al., 2005. J Proteome Res 4:972-981; Villanueva, et al., 2004. Anal Chem 76:1560-1570). Several recent reports have advocated the use of MS-based serum peptide profiling to determine qualitative and quantitative patterns, or ‘signatures’, that indicate the presence/absence of disease such as cancer (Petricoin, E. F., et al., 2002. Lancet 359:572-577; Adam, B. L., et al., 2002. Cancer Res 62:3609-3614; Li, J., et al., 2002. Clin Chem 48:1296-1304; Ebert, M. P., et al., 2004. J Proteome Res 3:1261-1266; Ornstein, D. K., et al. 2004. J Urol 172:1302-1305; Conrads, T. P., et al., 2004. Endocr Relat Cancer 11:163-178). To date, it has neither been accomplished to independently reproduce entire peptidomic patterns, nor has it been shown that the highly discriminatory peptides have the same amino acid sequences.

TOF-MS is the most efficient mass analysis technique in terms of detection sensitivity and readily achieves high mass analysis at good mass accuracy (R. J. Cotter, Anal. Chem. 64 (21), 1027 (1992)). It is one of the few analysis techniques that combines high sensitivity, selectivity and specificity with speed of analysis. For example, TOF-MS can record a complete mass spectrum on a microsecond timescale.

Advances in MS-based serum peptide profiling can have important implications for cancer diagnostics.

SUMMARY OF THE INVENTION

It has now been determined that distinctive peptide patterns that correlate with clinically relevant outcomes can be established through mass spectrometry (MS). Methods of the present invention employ serum peptide profiles to identify various types of cancer.

The present invention provides peptide markers that are differentially present in the samples of cancer subjects and in the samples of control subjects. Measurement of these markers, alone or in combination, in patient samples provides information correlating with a probable diagnosis of human cancer or a negative diagnosis (e.g., normal or disease-free). Accordingly, further disclosed are methods and kits that employ these markers in diagnosing and monitoring cancer.

In one aspect, the present invention provides methods of diagnosing or monitoring cancer in a subject comprising measuring at least one peptide marker in a sample from the subject. The cancer can be cancer of the prostate, bladder, breast or thyroid. Peptide markers of the invention include but are not limited to complement C3f, ITIH4, clusterin, complement C4-alpha, fibrinopeptideA, bradykinin, APO A-I, APOA-IV, APO E, kininogen, factor XIII, transthyretin and fibrinogenA. Preferably, peptide markers for ITIH4, clusterin, complement C4-alpha, APO A-I, APO A-IV, APO E, kininogen, factor XIII, transthyretin and fibrinogenA are present in the serum as peptide fragments.

In one embodiment, peptide marker levels are detected in a combination of two or more of the aforementioned peptide markers. Thus, the number of individual peptide markers measured in a sample can range from about 2 to 10, 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45, 45 to 50 and greater than about 50. In specific embodiments, at least about 20 of the peptide markers are measured.

In one embodiment, the invention provides a method of identifying cancer of the prostate in a subject comprising detecting an increase in a complement C3f peptide or a fragment thereof, a ITIH4, clusterin, complement C4-alpha, kininogen or factor XIII peptide fragment, or any combination thereof in a biological sample obtained from the subject, thereby identifying cancer of the prostate in the subject. The method can further comprise detecting a decrease in fibrinopeptideA peptide or a fragment thereof, or a fibrinogen-alpha peptide fragment, or any combination thereof in a biological sample obtained from the subject.

In another embodiment, the invention provides a method of identifying cancer of the bladder in a subject comprising detecting an increase in a complement C3f peptide or a fragment thereof, a ITIH4, clusterin, complement C4-alpha, fibrinogen-alpha, APO A-I, APO A-IV, APO E or kininogen peptide fragment, or any combination thereof in a biological sample obtained from the subject, thereby identifying cancer of the bladder in the subject. The method can further comprise detecting a decrease in a fibrinopeptideA peptide, bradykinin peptide, or a fragment thereof, a C4-alpha, ITIH4, or fibrinogen-alpha peptide fragment, or any combination thereof in a biological sample obtained from the subject.

In yet another embodiment, the invention provides a method of identifying cancer of the breast in a subject comprising detecting an increase in a fibrinopeptideA peptide, bradykinin peptide, or a fragment thereof, a ITIH4, complement C4-alpha, fibrinogen-alpha, APO A-IV, factor XIII or transthyretin peptide fragment, or any combination thereof in a biological sample obtained from the subject, thereby identifying cancer of the breast in the subject. The method can further comprise detecting a decrease in a fibrinopeptideA peptide, complement C3f peptide, or a fragment thereof, or any combination thereof in a biological sample obtained from the subject.

In yet another embodiment, the invention provides a method of identifying cancer of the prostate in a subject comprising detecting a decrease in a fibrinopeptideA peptide or a fragment thereof and a fibrinogen-alpha peptide fragment and an increase in a complement C3f peptide or a fragment thereof, a ITIH4, clusterin, complement C4-alpha, kininogen and factor XIII peptide fragment in a biological sample obtained from the subject, thereby identifying cancer of the prostate in the subject.

In yet another embodiment, the invention is provides a method of identifying cancer of the bladder in a subject comprising detecting a decrease in a fibrinopeptideA peptide, bradykinin peptide, or a fragment thereof, a C4-alpha, ITIH4, and fibrinogen-alpha peptide fragment and an increase in a complement C3f or a fragment thereof, a ITIH4, dusted % complement C4-alpha, fibrinogen-alpha, APO A-I, APO A-IV, APO E and kininogen peptide fragment in a biological sample obtained from the subject, thereby identifying cancer of the bladder in the subject.

In yet another embodiment, the invention provides a method of identifying cancer of the breast in a subject comprising detecting a decrease in a fibrinopeptideA peptide and complement C3f peptide, or a fragment thereof, and an increase in a fibrinopeptideA peptide, bradykinin peptide, or a fragment thereof, a ITIH4, complement C4-alpha, fibrinogen-alpha, APO A-IV, factor XIII and transthyretin peptide fragment in a biological sample obtained from the subject, thereby identifying cancer of the breast in the subject.

In specific embodiments of the invention concerning cancer of the prostate, the fibrinopeptideA peptide fragment that is decreased includes but is not limited to DSGEGDFLAEGGGVR (SEQ ID NO. 1), SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), EGDFLAEGGGVR (SEQ ID NO. 4), GDFLAEGGGVR (SEQ ID NO. 5), DFLAEGGGVR (SEQ ID NO. 6) or LAEGGGUR (SEQ ID NO. 25).

In other specific embodiments of the invention concerning cancer of the bladder, the fibrinopeptideA peptide fragment that is decreased includes but is not limited to DSGEGDFLAEGGGVR (SEQ ID NO. 1), SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), EGDFLAEGGGVR (SEQ ID NO. 4), GDFLAEGGGVR (SEQ ID NO. 5), DFLAEGGGVR (SEQ ID NO. 6), FLAEGGGUR (SEQ ID NO. 24) or LAEGGGUR (SEQ ID NO. 25).

In other specific embodiments of the invention concerning cancer of the breast, the fibrinopeptideA peptide fragment that is decreased includes but is not limited to SGEGDFLAEGGGVR (SEQ ID NO. 2) or GEGDFLAEGGGVR (SEQ ID NO. 3) and the fibrinopeptideA peptide fragment that is increased is FLAEGGGUR (SEQ ID NO. 24).

In other specific embodiments of the invention concerning cancer of the prostate, the complement C3f peptide fragment that is increased includes but is not limited to SSKITHRIHWESASLL (SEQ ID NO. 8), SKITHRIHWESASLL (SEQ ID NO. 9), KITHRIHWESASLL (SEQ ID NO. 10), THRIHWESASLL (SEQ ID NO. 11) or IHWESASLL (SEQ ID NO. 28).

In other specific embodiments of the invention concerning cancer of the bladder, the complement C3f peptide fragment that is increased includes but is not limited to SSKITHRIHWESASLL (SEQ ID NO. 8), SKITHRIHWESASLL (SEQ ID NO. 9), KITHRIHWESASLL (SEQ ID NO. 10), THRIHWESASLL (SEQ ID NO. 11), HWESASLL (SEQ ID NO. 12), RIHWESASLL (SEQ ID NO. 27), IHWESASLL (SEQ ID NO. 28) or SSKITHRIHWESASL (SEQ ID NO. 29).

In other specific embodiments of the invention concerning cancer of the breast, the complement C3f peptide fragment that is decreased includes but is not limited to SSKITHRIHWESASLL (SEQ ID NO. 8), HWESASLL (SEQ ID NO. 12) or ITHRIHWESASLL (SEQ ID NO. 26).

In other specific embodiments of the invention concerning cancer of the prostate, ITIH4 peptide fragment that is increased includes but is not limited to PGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 13), SRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 15), HAAYHPFR (SEQ ID NO. 34), QLGLPGPPDVPDHAAYHPFR (SEQ ID NO. 35), HAAYHPF (SEQ ID NO. 39), NVHSGSTFFKYYLQGAKIPKPEASFSPR (SEQ ID NO. 40) or NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41).

In other specific embodiments of the invention concerning cancer of the bladder, the ITIH4 peptide fragment that is increased includes but is not limited to PGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 13), SRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 15), HAAYHPFR (SEQ ID NO. 34), QAGAAGSRMNFRPGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 36), MNFRPGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 37), NVHSGSTFFKYYLQGAKIPKPEASFSPR (SEQ ID NO. 40) or NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41) and the ITIH4 peptide fragment that is decreased includes but is not limited to GVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 14) or HAAYHPF (SEQ ID NO. 39).

In other specific embodiments of the invention concerning cancer of the breast, the ITIH4 peptide fragment that is increased includes but is not limited to GLPGPPDVPDHAAYHPF (SEQ ID NO. 16), HAAYHPFR (SEQ ID NO. 34), QLGLPGPPDVPDHAAYHPFR (SEQ ID NO. 35), SSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 38) or NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41).

In other specific embodiments of the invention concerning cancer of the prostate, the clusterin peptide fragment includes but is not limited to HFFFPKSRIV (SEQ ID NO. 17).

In other specific embodiments of the invention concerning cancer of the bladder, the clusterin peptide fragment that is increased includes but is not limited to HFFFPKSRIV (SEQ ID NO. 17) or HFFFPK (SEQ ID NO. 18).

In other specific embodiments of the invention concerning cancer of the bladder, the bradykinin peptide fragment that is decreased includes but is not limited to RPPGFSPFR (SEQ ID NO. 19) or RPPGFSPF (SEQ ID NO. 20).

In other specific embodiments of the invention concerning cancer of the breast, the bradykinin peptide fragment that is increased includes but is not limited to RPPGFSPFR (SEQ ID NO. 19) or RPPGFSPF (SEQ ID NO. 20).

In other specific embodiments of the invention concerning cancer of the prostate, the complement C4-alpha peptide fragment that is increased includes but is not limited to GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23).

In other specific embodiments of the invention concerning cancer of the bladder, the complement C4-alpha peptide fragment that is increased includes but is not limited to RNGFKSHALQLNNRQI (SEQ ID NO. 21), GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23), or NGFKSHALQLNNR (SEQ ID NO. 31) and the complement C4-alpha peptide fragment that is decreased is GLEEELQFSLGSKINV (SEQ ID NO. 33).

In other specific embodiments of the invention concerning cancer of the breast, the complement C4-alpha peptide fragment that is increased includes but is not limited to RNGFKSHALQLNNRQI (SEQ ID NO. 21), NGFKSHALQLNNRQI (SEQ ID NO. 22), GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23), NGFKSHALQLNNRQ (SEQ ID NO. 30), GLEEELQFSLGSKINVKVGGNSKGTL (SEQ ID NO. 32) or GLEEELQFSLGSKINV (SEQ ID NO. 33).

In other specific embodiments of the invention concerning cancer of the prostate, the fibrinogen-alpha peptide fragment that is decreased includes but is not limited to SSSYSKQFTSSTSYNRGDSTFESKSYKMA (SEQ ID NO. 55) or SSSYSKQFTSSTSYNRGDSTFESKSYKM (SEQ ID NO. 56).

In other specific embodiments of the invention concerning cancer of the bladder, the fibrinogen-alpha peptide fragment that is increased includes but is not limited to SSSYSKQFTSSTSYNRGDSTFESKSYKMA (SEQ ID NO. 55), SSSYSKQFTSSTSYNRGDSTFESKSYKM (SEQ ID NO. 56), SSSYSKQFTSSTSYNRGDSTFESKSY (SEQ ID NO. 57), SSSYSKQFTSSTSYNRGDSTFESKS (SEQ ID NO. 58), or SSYSKQFTSSTSYNRGDSTFE (SEQ ID NO. 60), and the fibrinogen-alpha peptide fragment that is decreased is GSESGIFTNTKESSSHHPGIAEFPSRG (SEQ ID NO. 61).

In other specific embodiments of the invention concerning cancer of the breast, the fibrinogen-alpha peptide fragment that is increased includes but is not limited to SSYSKQFTSSTSYNRGDSTFE (SEQ ID NO. 60) or DEAGSEADHEGTHSTKRGHAKSRPV (SEQ ID NO. 62).

In other specific embodiments of the invention concerning cancer of the prostate, the kininogen peptide fragment is NLGHGHKHERDQGHGHQ (SEQ ID NO. 52).

In other specific embodiments of the invention concerning cancer of the bladder, the kininogen peptide fragment that is increased includes but is not limited to KHNLGHGHKHERDQGHGHQ (SEQ ID NO. 51) or NLGHGHKHERDQGHGHQ (SEQ ID NO. 52).

In other specific embodiments of the invention concerning cancer of the bladder, the APO A-I peptide fragment that is increased includes but is not limited to QGLLPVLESFKVSFLSALEEYTKKLNTQ (SEQ ID NO. 42), VSFLSALEEYTKKLNTQ (SEQ ID NO. 43) or ATEHLSTLSEKAKPALEDL (SEQ ID NO. 44).

In other specific embodiments of the invention concerning cancer of the bladder, the APO A-IV peptide fragment that is increased includes but is not limited to GNTEGLQKSLAELGGHLDQQVEEFR (SEQ ID NO. 46), SLAELGGHLDQQVEEFR (SEQ ID NO. 47) or SLAELGGHLDQQVEEF (SEQ ID NO. 48).

In other specific embodiments of the invention concerning cancer of the breast, the APO A-IV peptide fragment that is increased is ISASAEELRQRLAPLAEDVRGNL (SEQ ID NO. 45).

In other specific embodiments of the invention concerning cancer of the bladder, the APO E peptide fragment that is increased includes but is not limited to AATVGSLAGQPLQERAQAWGERLR (SEQ ID NO. 49) or AATVGSLAGQPLQERAQAWGERL (SEQ ID NO. 50).

In other specific embodiments of the invention concerning cancer of the prostate, the factor XIII peptide fragment that is increased is AVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO. 53).

In other specific embodiments of the invention concerning cancer of the breast, the factor XIII peptide fragment that is increased is AVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO. 53).

In other specific embodiments of the invention concerning cancer of the breast, the transthyretin peptide fragment that is increased is ALGISPFHEHAEVVFTANDSGPR (SEQ ID NO. 54).

In practicing the methods of the invention, the biological sample can comprise plasma or serum or a preparation thereof. Detection can comprise analyzing the biological sample, or a preparation thereof using mass spectrometry. The mass spectrometry can be MALDI TOF, Fourier-transform ion cyclotron resonance, electrospray ionization mass spectrometry, or combinations thereof. In another aspect, detection can comprise analyzing the biological sample, or a preparation thereof on a solid support, wherein peptides in the sample bind to the solid support.

In another aspect, the invention provides peptide profiles indicative of cancer of the prostate, bladder, and breast.

In one embodiment, the invention provides an isolated or identified peptide profile indicating cancer of the prostate comprising an increased amount of peptides or peptide fragments of SSKITHRIHWESASLL (SEQ ID NO. 8), SKITHRIHWESASLL (SEQ ID NO. 9), KITHRIHWESASLL (SEQ ID NO. 10), THRIHWESASLL (SEQ ID NO. 11), PGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 13), SRQLGLPGPPDVPDHAAYHPF (SEQ. ID NO. 15), HFFFPKSRIV (SEQ ID NO. 17), GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23), IHWESASLL (SEQ ID NO. 28), HAAYHPFR (SEQ ID NO. 34), QLGLPGPPDVPDHAAYHPFR (SEQ ID NO. 35), HAAYHPF (SEQ ID NO. 39), NVHSGSTFFKYYLQGAKIPKPEASFSPR (SEQ ID NO. 40), NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41), NLGHGHKHERDQGHGHQ (SEQ ID NO. 52), AVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO. 53), or combinations thereof. In an additional embodiment, the isolated or identified peptide profile indicating cancer of the prostate comprises a decreased amount of peptides or peptide fragments of DSGEGDFLAEGGGVR (SEQ ID NO. 1), SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), EGDFLAEGGGVR (SEQ ID NO. 4), GDFLAEGGGVR (SEQ ID NO. 5), DFLAEGGGVR (SEQ ID NO. 6), LAEGGGUR (SEQ ID NO. 25), SSSYSKQFTSSTSYNRGDSTFESKSYKMA (SEQ ID NO. 55), SSSYSKQFTSSTSYNRGDSTFESKSYKM (SEQ ID NO. 56), or combinations thereof.

In another embodiment, the invention provides an isolated or identified peptide profile indicating cancer of the bladder comprising an increased amount of peptides or peptide fragments of SSKITHRIHWESASLL (SEQ ID NO. 8), SKITHRIHWESASLL (SEQ ID NO. 9), KITHRIHWESASLL (SEQ ID NO. 10), THRIHWESASLL (SEQ ID NO. 11), HWESASLL (SEQ ID NO. 12), PGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 13), SRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 15), HFFFPKSRIV (SEQ ID NO. 17), HFFFPK (SEQ ID NO. 18), RNGFKSHALQLNNRQI (SEQ ID NO. 21), GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23), (SEQ ID NO. 27), IHWESASLL (SEQ ID NO. 28), SSKITHRIHWESASL (SEQ ID NO. 29), NGFKSHALQLNNR (SEQ II) NO. 31), HAAYHPFR (SEQ ID NO. 34), QAGAAGSRMNFRPGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 36), MNFRPGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 37), NVHSGSTFFKYYLQGAKIPKPEASFSPR (SEQ ID NO. 40), NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41), QGLLPVLESFKVSFLSALEEYTKKLNTQ (SEQ ID NO. 42), VSFLSALEEYTKKLNTQ (SEQ ID NO. 43), ATEHLSTLSEKAKPALEDL (SEQ ID NO. 44), GNTEGLQKSLAELGGHLDQQVEEFR (SEQ ID NO. 46), SLAELGGHLDQQVEEFR (SEQ ID NO. 47), SLAELGGHLDQQVEEF (SEQ ID NO. 48), AATVGSLAGQPLQERAQAWGERLR (SEQ ID NO. 49), AATVGSLAGQPLQERAQAWGERL (SEQ ID NO. 50), KHNLGHGHKHERDQGHGHQ (SEQ ID NO. 51), NLGHGHKHERDQGHGHQ (SEQ ID NO. 52), GSESGIFTNTKESSSHHPGIAEFPSRG (SEQ ID NO. 61), or combinations thereof. In an additional embodiment, the isolated or identified peptide profile indicating cancer of the bladder comprises a decreased amount of peptides or peptide fragments of DSGEGDFLAEGGGVR (SEQ ID NO. 1), SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), EGDFLAEGGGVR (SEQ ID NO. 4), GDFLAEGGGVR (SEQ ID NO. 5), DFLAEGGGVR (SEQ ID NO. 6), GVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 14), RPPGFSPFR (SEQ ID NO. 19), RPPGFSPF (SEQ ID NO. 20), FLAEGGGUR (SEQ ID NO. 24), LAEGGGUR (SEQ ID NO. 25), GLEEELQFSLGSKINV (SEQ ID NO. 33), HAAYHPF (SEQ ID NO. 39), SSSYSKQFTSSTSYNRGDSTFESKSYKMA (SEQ ID NO. 55), SSSYSKQFTSSTSYNRGDSTFESKSYKM (SEQ ID NO. 56), SSSYSKQFTSSTSYNRGDSTFESKSY (SEQ ID NO. 57), SSSYSKQFTSSTSYNRGDSTFESKS (SEQ ID NO. 58), SSYSKQFTSSTSYNRGDSTFE (SEQ ID NO. 60), or combinations thereof.

In yet another embodiment, the invention provides an isolated or identified peptide profile indicating cancer of the breast comprising an increased amount of peptides or peptide fragments of GLPGPPDVPDHAAYHPF (SEQ ID NO. 16), RPPGFSPFR (SEQ ID NO. 19), RPPGFSPF (SEQ ID NO. 20), RNGFKSHALQLNNRQI (SEQ ID NO. 21), NGFKSHALQLNNRQI (SEQ ID NO. 22), GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23), FLAEGGGUR (SEQ ID NO. 24), NGFKSHALQLNNRQ (SEQ ID NO. 30), GLEEELQFSLGSKINVKVGGNSKGTL (SEQ ID NO. 32), GLEEELQFSLGSKINV (SEQ ID NO. 33), HAAYHPFR (SEQ ID NO. 34), QLGLPGPPDVPDHAAYHPFR (SEQ ID NO. 35), SSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 38), NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41), ISASAEELRQRLAPLAEDVRGNL (SEQ ID NO. 45), AVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO. 53), ALGISPFHEHAEVVFTANDSGPR (SEQ ID NO. 54), SSYSKQFTSSTSYNRGDSTFE (SEQ ID NO. 60), DEAGSEADHEGTHSTKRGHAKSRPV (SEQ ID NO. 62), or combinations thereof. In an additional embodiment, the isolated or identified peptide profile indicating cancer of the breast comprises a decreased amount of peptides or peptide fragments of SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), SSKITHRIHWESASLL (SEQ ID NO. 8), HWESASLL (SEQ ID NO. 12), ITHRIHWESASLL (SEQ ID NO. 26), or combinations thereof.

In one embodiment of the peptide profile of the invention, the profile is present in an isolated biological sample. In another embodiment, the identified profile is stored by electronic means.

In one aspect, the invention provides a method of generating a peptide profile of a subject having, or at risk of having, cancer of the prostate, comprising the steps of:

i) combining an exogenous peptide including but not limited to a complement C3f, ITIH4, clusterin, complement C4-alpha, fibrinopeptide A, kininogen, factor XIII, and fibrinogenA peptide or a combination thereof with a biological sample from the subject; and

ii) proteolytically digesting a peptide of step i),

thereby generating a peptide profile of the subject.

In additional embodiments of the invention, the peptide profile indicates that the subject has or is at risk of having cancer of the prostate.

In one aspect, the invention provides a method of generating a peptide profile of a subject having, or at risk of having, cancer of the bladder, comprising the steps of:

i) combining an exogenous peptide including but not limited to a complement C3f, ITIH4, clusterin, complement C4-alpha, fibrinopeptide A, bradykinin, APO A-I, APO A-IV, APO E, kininogen, and fibrinogenA peptide or a combination thereof with a biological sample from the subject; and

ii) proteolytically digesting a peptide of step i), thereby generating a peptide profile of the subject.

In an additional embodiment of the invention, the peptide profile indicates that the subject has or is at risk of having cancer of the bladder.

In one aspect, the invention provides a method of generating a peptide profile of a subject having, or at risk of having, cancer of the breast, comprising the steps of:

i) combining an exogenous peptide including but not limited to a ITIH4, bradykinin, complement C4-alpha, fibrinopeptide A, complement C3f, APO A-IV, factor XIII, transthyretin and fibrinogenA peptide or a combination thereof with a biological sample from the subject; and

ii) proteolytically digesting a peptide of step i), thereby generating a peptide profile of the subject.

In an additional embodiment of the invention, the peptide profile indicates that the subject has or is at risk of having cancer of the breast.

In one aspect, the invention is provides a method of generating a peptide profile of a subject having, or at risk of having, cancer of the thyroid, comprising the steps of:

i) combining an exogenous peptide selected from the group consisting of a fibrinopeptide A, fibrinogenA, complement C3f peptide and combinations thereof with a biological sample from the subject, and

ii) proteolytically digesting a peptide of step i),

thereby generating a peptide profile of the subject.

In an additional embodiment of the invention, the peptide profile indicates that the subject has or is at risk of having cancer of the thyroid.

In further embodiments of the invention, the exogenous peptide is labeled with an isotope. In yet further embodiments of the invention, the biological sample is serum or plasma. In yet further embodiments of the invention, the exogenous peptide is a synthetic peptide. In yet further embodiments of the invention, the exogenous peptide is comprised of D-amino acids. In yet further embodiments of the invention, the proteolytic digest is analyzed, for example, using mass spectrometry.

Methods of the invention can further comprise the step of obtaining the exogenous peptide.

In yet another aspect, the invention provides a kit for generating a peptide profile of a subject having, or at risk of having, cancer of the bladder, breast, prostate or thyroid comprising an exogenous peptide or peptide fragment selected from the group consisting of complement C3f peptide, ITIH4 peptide, clusterin peptide, complement C4-alpha peptide, fibrinopeptideA peptide, bradykinin peptide, APO A-I peptide, APOA-IV peptide, APO E peptide, kininogen peptide, factor XIII peptide, transthyretin peptide and fibrinogenA peptide and instructions for use and/or a packaging means thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A shows the color-coding scheme followed in the representation of data collected for the blood samples from healthy volunteers (n=33) and from patients with advanced prostate (n=32), bladder (n=20) and breast (n=21) cancer.

FIG. 1B shows the results of unsupervised, average-linkage hierarchical clustering performed using standard correlation as a distance metrics (‘GeneSpring’ program), between each cancer group and the control, in binary format. The entire peak list (651×106) was used. Columns represent samples; rows are m/z-peaks (i.e., peptides). Dendrogram colors follow the color-coding scheme of panel A. The heat map scale of normalized intensities is from 0 (green) to 200 (red), with the midpoint at 100 (yellow).

FIG. 1C shows the results of hierarchical clustering performed for the three cancer groups plus control (as in 1B, above).

FIG. 1D shows the results of Principal Component Analysis (PCA) of the three cancer groups plus controls based on the full peak list. Color-coding is as in panel A. The first three principal components, accounting for most of the variance in the original data set are shown.

FIG. 2A shows pie charts depicting the peak number reduction in three m/z ranges, which illustrates the impact of each filter on peptides of different molecular mass.

FIG. 2B depicts the Venn-diagrams showing the number of peptides that passed two selection steps. m/z-peaks with higher intensities in one (or more) of the cancer groups as compared to controls are shown in the left panel, while those with lower intensities are shown in the right panel. The numbers shown outside the diagrams indicate the total number of peptides of a specific cancer group that were either up or down.

FIG. 2C shows heat maps comparing the selected features of the three cancer groups with controls in multi-class and binary formats. Columns represent samples (as indicated per group); rows are peptide m/z-peaks (not in numerical order). The number of peptides used in each binary comparison (i.e., 58, 14, and 14) is the sum of those that were specifically higher and lower in each cancer group; the multi-class heat map contains the total, non-redundant number of peptides (i.e., 68). The ‘multi-class’, ‘bladder’ and ‘breast’ heat map scales of normalized intensities are from 0 (green) to 500 (red), with the midpoint at 250 (yellow); those of the ‘prostate’ heat map are, respectively, 0, 2,000 and 1,000.

FIG. 2D depicts overlays of mass spectra obtained from the three binary comparisons (cancer vs. control). Mono-isotopic masses are listed for each peak. Two statistically significant differences in peptide intensities (one higher; one lower) between prostate cancer (blue) and controls (yellow) are shown, as well as one higher-intensity peptide for bladder cancer (green) and one for breast cancer (red).

FIGS. 3A and 3B show MALDI-TOF mass spectral overlays of selected peaks derived from serum peptide profiling of three groups of cancer patients and healthy controls. Each overlay shows a binary comparison for all spectra from either the bladder cancer (n=20; green), or prostate cancer (n=32; blue) or breast cancer patient group (n=21; red) versus the control group (n=33; yellow). They are arrayed in a way that the same mass range window is shown for each of the three binary comparisons, in which spectral intensities were normalized and scaled to the same size, except for ‘2021.05’, which is included herein as an example of the vast majority of peptide-ions with intensities not statistically different between any two groups. (A) Overlays of mass spectra of selected peptides of known sequence (see FIG. 3) that showed statistically significant differences between peak intensities in one or more of the three binary comparisons. The mono-isotopic mass (m/z) of the peak is shown for each peptide. (B) Overlays of mass spectra of some as yet unidentified peptides that also showed statistically significant differences between peak intensities in one or more of the three binary comparisons. The bin ‘name’ (a number that is close to the average isotopic mass) is shown for each peptide.

FIG. 4 shows a fragment ion spectrum for MALDI-TOF/TOF MS/MS identification of serum peptide 2305.20 as a fragment of complement 4a. b″- and y″-fragment ion series are indicated, together with the limited sequences (above arrows). Note that y″-ions originate at the C-terminus and that the sequence therefore reads backwards (see direction of the arrows).

FIG. 5A lists the groups (‘ladders’) of overlapping sequences of the peptides identified by MALDI-TOF/TOF MS/MS. Taken together, 61 peptide-ions on the list have clear peptide-ion marker potential (adjusted p<0.0002; see FIG. 5B, below) for at least one type of cancer and are color-coded in blue (prostate cancer), green (bladder cancer) or red (breast cancer). The resulting ‘barcodes’ for the three cancer types consist of 26 (prostate), 50 (bladder) and 25 (breast) peptide-ions. Color-coded peptides have either higher (no dot) or lower (black dot) differential ion intensities in a particular cohort of cancer samples as compared to controls. Of the 8 non-markers listed here, full-length C3f (m/z=2021.05) and one member of the fibrinogen-alpha cluster (m/z=2553.01) gave comparable ion signals in all patient group and control sera (see FIG. 5B; FIG. 3, ‘2021’), and, therefore, represent virtual internal standards (yellow-coded). Six peptides (pink-coded) in the clusters were randomly observed in samples of the cancer and control groups and have neither discriminant nor internal control value. Note that the measured m/z values, as listed, are mono-isotopic and, therefore, smaller than the corresponding average isotopic values in FIG. 13 a. Amino acids in brackets were not experimentally observed but are shown to either indicate putative full-length sequences of the founders, each resulting from specific proteolyis of precursor proteins, and/or of the positions of the putative ‘trypsin-like’ cleavage sites (Arg/Lys-Xaa).

FIG. 5B depicts a table listing additional details of the identified peptides as m/z values, MS-ion intensities, and ‘barcodes’ (blue, green or red—as described above). The actual barcodes (blue, green or red) are composed of entries that showed clear peptide-ion marker potential (adjusted p<0.0002) for at least one type of cancer. Adjusted p-value is the overriding criterion, leading to final barcodes of 26 (prostate), 50 (bladder) and 25 (breast) peptide-ions. The second column lists median intensities of each m/z-peak in the control samples. Peak intensity ratios (columns 3-5) were calculated by dividing the median values of each m/z-peak in each cancer group by the median value of the corresponding peak in the control samples. Ratios (r) for the peptides that are part of one or more barcodes are shaded; dark grey when the median signal was of higher intensity in a particular cancer (r≧1.6), lighter grey when it was lower (r≧0.66). The significance levels (p values) of three different one-way ANOVA Mann-Whitney tests (columns 6-8) and of a multi-class Kluskal-Wallis test (column 9) are given. C3f (coded yellow) has virtually no discriminant value.

FIG. 6 shows, in bar graph form, the median intensity for each serum peptide in each of the three cancer groups (color-coding as indicated) plotted as the ratio versus the median intensity of the counterpart in the control group (r=case/control). Ratios are plotted on a log scale ranging from 0.1 to 10. Bars pointing to the left (r<1) or right (r>1) indicate, respectively, lower or higher median intensities in a cancer group as in the control group. Peptides that didn't show much difference in median ion intensity between case and control groups map closely to or onto the centerline (r=1).

FIG. 7 shows a flow chart-type diagram delineating the approach used for development and validation of (i) the 68-peptide-ion signature and (ii) the prostate cancer barcode consisting of 26 serum peptides with known sequence (blue-coded in FIG. 5). Numbers that are encircled indicate total number of selected peptides at that stage of the study.

FIG. 8A schematically depicts the independent prostate cancer serum sample groups identified for the validation of the established biomarkers.

FIGS. 8B and 8C show the results of Hierarchical Cluster (HCA) and Principal Component (PCA) Analyses of all spectra from the Prostate #1 (blue), Prostate #2 (cyan) and control groups (yellow). Two limited sets of peptide-ions were used for the analyses: the 68 combined peptides that had statistically significant differences in intensity for the three binary comparisons (FIG. 2B; FIG. 17) (left), and the 26 sequenced peptides that constitute the prostate cancer barcode (color-coded blue in FIG. 5) (right). The rest of the ˜650 peptide-ions were ignored for the cluster analysis. Dendrogram colors follow the color-coding scheme of panel A. The heat map scale of normalized ion-intensities is from 0 (green) to 2,000 (red), with the midpoint at 1,000 (yellow). For the PCA, the first three principal components, accounting for most of the variance in the original data set, are shown.

FIG. 8D shows a table listing the results of class prediction analysis of the prostate cancer validation set (Prostate #2) using Support Vector Machine (SVM) and either all 651 m/z-values or the 68-, 26-feature sets described above. Analyses were done using linear kernel. The proportions of correct predictions are listed. The binomial confidence intervals (at 95%) were 87.1-99.9% for 40 correct predictions out of 41, and 91.4-100% for 41/41. The training sets were either Prostate #1 versus control (‘binary’) or the 3 cancer groups (Prostate #1, bladder and breast cancer) plus controls (‘multi-class’).

FIG. 9 shows MALDI-TOF MS read-outs of fresh plasma (top panel), indicating very low levels of small peptides, except for bradykinin and desArg-bradykinin, of an aliquot withdrawn immediately (i.e., after 15-20 s) after addition of synthetic C3f (1 pmole/μL plasma) (middle panel, indicating removal of the C-terminal Arg, by a carboxypeptidase, in a matter of seconds), and of an aliquot withdrawn after another 15 minutes at room temperature (lower panel, indicating that C3f is then further degraded by the activity of aminopeptidases to result in a type of sequence ladder as endogenously present in serum).

FIG. 10 schematically depicts the activity of serum proteases. Amino acids are color-coded to represent sequence clusters of C3f (left) or FPA (right), which are just two examples of all the observed clusters.

FIG. 11A graphically depicts the distribution of serum peptides. Number of m/z-peaks are plotted as a function of m/z range. The first bin, from m/z=0 to 700, is empty, as no data was collected in that region. No bins are shown in the range >10 kDa.

FIG. 11B likewise graphically depicts the distribution of serum peptides. Here, however, number of m/z-peaks are plotted as a function of normalized intensity. No bins are shown in the region over 1,000 arbitrary units. The highlighted area indicates the range above the median peak-intensity threshold, used for selecting potential biomarkers (FIG. 17).

FIG. 12 depicts a histogram that shows, starting with a total of 651 unique m/z-peaks (blue bars) derived from three groups of cancer patients and healthy controls, the number of peptides in each mass range that passed two filters applied during feature selection.

FIG. 13A shows a table listing averages plus (±) standard deviations and medians (in brackets) of the intensities of each m/z-peak (i.e., serum peptide) within a particular data set derived from each of the three cancer patient groups and of the healthy controls. Intensities refer to normalized units that were calculated for each peak by dividing its raw intensity by the total of all of the intensities in that spectrum (TIC—Total Ion Count). The resultant values were then multiplied by fixed scaling factor (1×10⁷) to convert the data to a ‘user-friendly’ scale (i.e. most values ≧1).

FIG. 13B shows a table listing ratios calculated by dividing the median normalized intensity of each m/z-peak in each cancer group by the median of the same m/z-peak in the control group. To avoid having to divide by zero, any median value of less than was converted to 1. This was applied to all groups. Data for a second, independent validation set of prostate cancer samples is also listed.

FIG. 13C shows a table listing the false discovery rate adjusted p-values calculated for each m/z-peak using the Mann-Whitney rank sum test (for binary comparisons) or the Kruskal-Wallis test (for multi-class comparisons). The group of 68 m/z-peaks listed were derived from the original peak list, containing normalized ion intensities (and medians within a group, case/control ratios and adjusted p-values) for each of the 651 m/z-peaks for each of the 106 samples, by applying p-value and median intensity cut-off filters (p<0.00001; median intensity ≧500 ‘units’). Entries which passed both filters in one or more cancer groups are color-coded: prostate cancer (14; blue), breast cancer (14; red) and bladder cancer (58; green).

FIG. 14 shows a table listing the total serum peptide sequences, organized per overlapping cluster; with clusters organized per precursor protein (NCBI ID nos. are given). Positions in the precursor proteins are indicated. Residues between brackets were not observed but are listed in the present table to indicate the putative primary cleavage sites by endoproteases. Additional information is given, as for instance the relative position of adjacently located peptides or peptide clusters, identity of previously known serum petides (e.g., FPA, C3f), position of propeptides, and location of C-termini (C-t). Key: Met_(ox) or M_(ox), oxidized methionine; Pro_(hydroxyl), hydroxylated proline.

FIG. 15 shows a table listing the locations of sequenced serum peptides in the precursor proteins. NCBI ID nos. are given, as well as the positions of known, processed serum proteins, peptides and propeptides. The peptide sequences obtained herein are shown in bold and are underlined.

FIG. 16A shows, in table form, the data set of 651 unique m/z-peaks derived from MALDI-TOF MS serum peptide profiling of three groups of cancer patients and healthy controls. Presented are the averages plus (±) standard deviations and the median values (in brackets) of the intensities of each m/z-peak (i.e., serum peptide) within a particular data set derived from each of the three cancer patient groups and of the healthy controls; a second, independent validation set of prostate cancer samples is also listed. Intensities refer to normalized units that were calculated for each peak by dividing its raw intensity by the total of all the intensities in that spectrum (TIC—Total Ion Count). The resultant values were then multiplied by fixed scaling factor (1×10⁷) to convert the data to a ‘user-friendly’ scale (i.e. most values ≧1).

FIG. 16B shows, in table form, the data set of 651 unique m/z-peaks derived from MALDI-TOF MS serum peptide profiling of three groups of cancer patients and healthy controls.

FIG. 16C shows, in table form, the data set of 651 unique m/z-peaks derived from MALDI-TOF MS serum peptide profiling of three groups of cancer patients and healthy controls.

FIGS. 17A, 17B, and 17C show, in table form, the data set of 68 putative biomarker m/z-peaks, derived from MALDI-TOF MS serum peptide profiling of three groups of cancer patients and healthy controls. The figures contain (i) means plus (±) standard deviations, and medians (in brackets); (ii) discriminant analysis false positive rates (p-values); and (iii) ratios of the median intensities in a group for all 68 m/z-peaks retained after applying p-value and median intensity cutoff filters (p<0.00001; median intensity ≧500 units). All values were extracted from FIGS. 16A-C, above. Entries which passed both filters in one or more cancer groups are color-coded: prostate cancer (14; blue), breast cancer (14; red) and bladder cancer (58; green).

FIG. 18 shows SEQ ID NO:63, GENBANK Accession No. AAH00664, C3F protein (Homo sapiens), amino acid residues 1-436.

FIG. 19 shows SEQ ID NO:64, GENBANK Accession No. Q14624, Inter-alpha-trypsin inhibitor heavy chain H4 precursor (ITI heavy chain H4) (Homo sapiens), amino acid residues 1 to 930, wherein 29-661=“70 kDa inter-alpha-trypsin inhibitor heavy chain H4” and 689-930=“35 kDa inter-alpha-trypsin inhibitor heavy chain H4.”

FIG. 20 shows SEQ ID NO:65, GENBANK Accession No. AAP88927, clusterin (complement lysis inhibitor (Homo sapiens), amino acid residues 1 to 447.

FIG. 21 shows SEQ ID NO:66, GENBANK Accession No. AAR89159, C4A (Homo sapiens), amino acid residues 1 to 534.

FIG. 22 shows SEQ ID NO:67, GENBANK Accession No. NP_(—)068657, fibrinogen, alpha chain isoform alpha preproprotein (Homo sapiens), amino acid residues 1 to 644, wherein 20-35 product=“fibrinopeptide A.”

FIG. 23 shows SEQ ID NO:68, GENBANK Accession No. P01042, kininogen precursor (Alpha-2-thiol proteinase inhibitor) (Homo sapiens), amino acid residues 1 to 644, wherein 381-389=“Bradykinin.”

FIG. 24 shows SEQ ID NO:69, GENBANK Accession No. NM_(—)021871, Homo sapiens fibrinogen alpha chain (FGA), transcript variant alpha, mRNA.

FIG. 25 shows SEQ ID NO:70, GENBANK Accession No. NM_(—)000039, Homo sapiens apolipoprotein A-I (APOA1), mRNA.

FIG. 26 shows SEQ ID NO:71, GENBANK Accession No. NM_(—)000482, Homo sapiens apolipoprotein A-IV (APOA4), mRNA.

FIG. 27 shows SEQ ID NO:72, GENBANK Accession No. NM_(—)000041, Homo sapiens apolipoprotein E (APOE), mRNA.

FIG. 28 shows SEQ ID NO:73, GENBANK Accession No. NM_(—)000893, Homo sapiens kininogen (KNG1).

FIG. 29 shows SEQ ID NO:74, GENBANK Accession No. NM_(—)000129, Homo sapiens coagulation factor XIII, A1 polypeptide (F13A1), mRNA.

FIG. 30 shows SEQ ID NO:75, GENBANK Accession No. NM_(—)000371, Homo sapiens transthyretin (prealbumin, amyloidosis type I)(TTR), mRNA.

FIG. 31 shows, in table form, 66 reference peptides. All amino acids are D-stereo-isomers, except for the isotope-containing (L-isomer). Isotope-labeled amino acids: L, ¹³C(6)-Leu; F, ¹³C(6-ring)-Phe; V, ¹³C(5)/¹⁵N(1)-Val. (Note: isotope labels result in a molecular mass increase by 6 Da for each peptide). Surrogate marker code: P, prostate cancer; B, breast cancer; BL, bladder cancer; T, thyroid cancer; +, median ion intensity of this particular peptide in MALDI-TOF MS is higher in cancer samples than in controls; −, median ion intensity lower in cancer than controls.

FIG. 32 shows the MALDI-based, relative quantitation of serum peptides: A, normalized ion intensities as spectral overlays and B, as a heat plot. C shows the relative quantitation of normalized ion intensities in bar graph form.

FIG. 33 shows, in table form, founder peptides. Total 15 syntheses, including 2 (#7 and 11) or more multi-samplings; ≧18 cleavages, purifications, QC and quantitation. Isotope-labeled amino acids: L, ¹³C(6)-Leu; F, ¹³C(6-ring)-Phe; V, ¹³C(5)/¹⁵N(1)-Val; A, ¹³C(3)/¹⁵N(1)-Ala; resulting in molecular mass increase of 12 Da per peptide.

FIG. 34A shows median ion intensities in MALDI spectra taken of breast cancer sera vs. control sera. FIG. 34B shows selected views of isotopically resolved or partially resolved peptide-ion peaks; red, breast cancer; black, controls.

FIG. 35 shows ten peptide-triplets and plots of the ratios between exogenously derived peptides and reference peptide calculated. Inset is a small section of the MALDI spectrum showing the position of the monoisotopic envelopes for each of the three iso-peptides.

DETAILED DESCRIPTION OF THE INVENTION I. Definitions

Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this invention belongs. As used herein, the following terms have the meanings ascribed to them unless specified otherwise.

A “subject” is a vertebrate, preferably a mammal, more preferably a primate and still more preferably a human. Mammals include, but are not limited to, primates, humans, farm animals, sport animals, and pets.

As used herein, “serum” refers to the fluid portion of the blood obtained after removal of the fibrin clot and blood cells, distinguished from the plasma in circulating blood. As used herein, “plasma” refers to the fluid, noncellular portion of the blood, distinguished from the serum obtained after coagulation.

As used herein, “sample” or “biological sample” refers to anything, which may contain an analyte (e.g., peptide) for which an analyte assay is desired. The sample may be a biological sample, such as a biological fluid or a biological tissue. Examples of biological fluids include urine, blood, plasma, serum, saliva, semen, stool, sputum, cerebral spinal fluid, tears, mucus, amniotic fluid or the like. Biological tissues are aggregates of cells, usually of a particular kind including, for example, connective, epithelium, muscle and nerve tissues. Examples of biological tissues also include organs, tumors, lymph nodes, arteries and individual cell(s).

The term “isolated” refers to one or more compositions obtained from and/or contained in a sample apart from the body.

The term “identified” as in an “identified peptide” or “peptide profile” refers to one or more compositions or information relating thereto (e.g., a peptide and its amino acid sequence information) obtained under conditions of selection. Such information may optionally be stored by electronic means.

As used herein, the terms “gene” and “recombinant gene” refer to nucleic acid molecules comprising an open reading frame encoding a marker protein.

“Gas phase ion spectrometer” refers to an apparatus that detects gas phase ions. Gas phase ion spectrometers include an ion source that supplies gas phase ions. Gas phase ion spectrometers include, for example, mass spectrometers, ion mobility spectrometers, and total ion current measuring devices. “Gas phase ion spectrometry” refers to the use of a gas phase ion spectrometer to detect gas phase ions.

“Mass spectrometer” refers to a gas phase ion spectrometer that measures a parameter that can be translated into mass-to-charge ratios of gas phase ions. Mass spectrometers generally include an ion source and a mass analyzer. Examples of mass spectrometers are time-of-flight, magnetic sector, quadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these. “Mass spectrometry” refers to the use of a mass spectrometer to detect gas phase ions.

“Laser desorption mass spectrometer” refers to a mass spectrometer that uses laser energy as a means to desorb, volatilize, and ionize an analyte.

“Tandem mass spectrometer” refers to any mass spectrometer that is capable of performing two successive stages of m/z-based discrimination or measurement of ions, including ions in an ion mixture. The phrase includes mass spectrometers having two mass analyzers that are capable of performing two successive stages of m/z-based discrimination or measurement of ions tandem-in-space. The phrase further includes mass spectrometers having a single mass analyzer that is capable of performing two successive stages of m/z-based discrimination or measurement of ions tandem-in-time. The phrase thus explicitly includes Qq-TOF mass spectrometers, ion trap mass spectrometers, ion trap-TOF mass spectrometers, TOF-TOF mass spectrometers, Fourier transform ion cyclotron resonance mass spectrometers, electrostatic sector—magnetic sector mass spectrometers, and combinations thereof.

“Mass analyzer” refers to a sub-assembly of a mass spectrometer that comprises means for measuring a parameter that can be translated into mass-to-charge ratios of gas phase ions. In a time-of-flight mass spectrometer the mass analyzer comprises an ion optic assembly, a flight tube and an ion detector.

The term “MALDI” is used herein to refer to Matrix-Assisted Laser Desorption/Ionization, a process wherein analyte is embedded in a solid or crystalline “matrix” of light-absorbing molecules (e.g., nicotinic, sinapinic, or 3-hydroxypicolinic acid), then desorbed by laser irradiation and ionized from the solid phase into the gaseous or vapor phase, and accelerated as intact molecular ions towards a detector. The “matrix” is typically a small organic acid mixed in solution with the analyte in a 10,000:1 molar ratio of matrix/analyte. The matrix solution can be adjusted to neutral pH before use.

The term “MALDI-TOF MS” is used herein to refer to Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry.

The term “MALDI ionization surface” is used herein to refer to a surface for presentation of matrix-embedded analyte into a mass spectrometer for MALDI. In general, the terms “probe” or “probe element” are used interchangeably to refer to a device for presenting analyte into a mass spectrometer for irradiation and desorption. Metals such as gold, copper and stainless steel are typically used to form MALDI ionization surfaces. However, other commercially-available inert materials (e.g., glass, silica, nylon and other synthetic polymers, agarose and other carbohydrate polymers, and plastics) can be used where it is desired to use the surface to actively capture an analyte or as a reaction zone for chemical modification of the analyte.

“Solid support” refers to a solid material, which can be derivatized with, or otherwise attached to, a capture reagent. Exemplary solid supports include probes, microtiter plates and chromatographic resins.

“Eluant” or “wash solution” refers to an agent, typically a solution, which is used to affect or modify adsorption of an analyte to an adsorbent surface and/or remove unbound materials from the surface. The elution characteristics of an eluant can depend on, for example, pH, ionic strength, hydrophobicity, degree of chaotropism, detergent strength and temperature.

“Monitoring” refers to recording changes in a continuously varying parameter (e.g. monitoring progression of a cancer).

“Biochip” refers to a solid substrate having a generally planar surface to which an adsorbent is attached. Frequently, the surface of the biochip comprises a plurality of addressable locations, each of which location has the adsorbent bound there. Biochips can be adapted to engage a probe interface, and therefore, function as probes.

“Protein biochip” refers to a biochip adapted for the capture of polypeptides.

The terms “polypeptide,” “peptide” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one or more amino acid residue is an analog or mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers. Polypeptides can be modified, e.g., by the addition of carbohydrate residues to form glycoproteins. The terms “polypeptide,” “peptide” and “protein” include glycoproteins, as well as non-glycoproteins.

An “exogenous peptide” is a peptide obtained from a biological source that is external to the subject's body or by synthetic means.

The terms “peptide”, “peptide marker”, “marker” and “biomarker” are used interchangeably in the context of the present invention and refer to a polypeptide which is differentially present in a sample taken from subjects having human cancer as compared to a comparable sample taken from control subjects (e.g., a person with a negative diagnosis or undetectable cancer, normal or healthy subject). The markers are identified by molecular mass in Daltons, and include the masses centered around the identified molecular masses for each marker.

The term “detecting” means methods which include identifying the presence or absence of marker(s) in the sample, quantifying the amount of marker(s) in the sample, and/or qualifying the type of biomarker. Detecting includes identifying the presence, absence or amount of the object to be detected (e.g. a serum peptide marker).

“Diagnostic” means identifying the presence or nature of a pathologic condition, i.e., cancer. While a particular diagnostic method may not provide a definitive diagnosis of a condition, it suffices if the method provides a positive indication that aids in diagnosis.

As used herein, the term “sensitivity” is the percentage of marker-detected subjects with a particular disease.

As used herein, the term “specificity” is the percentage of subjects correctly identified as having a particular disease i.e., normal or healthy subjects. For example, the specificity is calculated as the number of subjects with a particular disease as compared to non-cancer subjects (e.g., normal healthy subjects).

The phrase “differentially present” refers to differences in the quantity and/or the frequency of a marker present in a sample taken from subjects having human cancer as compared to a control subject. For example, serum peptide markers described herein are present at an elevated level in samples of subjects compared to samples from control subjects. In contrast, other markers described herein are present at a decreased level in samples of cancer subjects compared to samples from control subjects. Furthermore, a marker can be a polypeptide, which is detected at a higher frequency or at a lower frequency in samples of human cancer subjects compared to samples of control subjects. A marker can be differentially present in terms of quantity, frequency or both. A polypeptide is differentially present between two samples if the amount of the polypeptide in one sample is statistically significantly different from the amount of the polypeptide in the other sample. Alternatively or additionally, a polypeptide is differentially present between two sets of samples if the frequency of detecting the polypeptide in the cancer subjects' samples is statistically significantly higher or lower than in the control samples.

“Optional” or “optionally” means that the subsequently described feature or structure may or may not be present in the analysis system or that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said feature or structure is present and instances where the feature or structure is absent, or instances where the event or circumstance occurs and instances where it does not.

The term “obtaining” as in “obtaining the exogenous peptide” is intended to include purchasing, synthesizing or otherwise acquiring the exogenous (or indicated substance or material).

The terms “comprises”, “comprising”, and the like are intended to have the broad meaning ascribed to them in U.S. Patent Law and can mean “includes”, “including” and the like.

It is to be understood that this invention is not limited to the particular component parts of a device described or process steps of the methods described, as such devices and methods may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting. As used in the specification and the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly indicates otherwise. Thus, for example, reference to “an analyte” includes mixtures of analytes, reference to “a MALDI ionization surface” includes two or more such ionization surfaces, reference to “a microchannel” includes more than one such component, and the like. Furthermore, reference to “cancer” may signify cancer in general (i.e., cancer of any type) or cancer of a specific type. Accordingly, the description herein of a subject as having no detectable cancer may signify a subject in which a specific type of cancer (for example, bladder) is not detectable. However, such a description may not necessarily signify that the subject has no type of cancer whatsoever.

Other definitions appear in context throughout the specification.

II. Methods and Peptide Profiles of the Invention

The present invention provides peptide markers generated from comparisons of protein profiles from subjects diagnosed with cancer and from subjects without known neoplastic diseases. In particular, the invention provides that these markers, used individually or in combination with other markers, provide a method of diagnosing and monitoring cancer in a subject having cancer of the prostate, of the bladder, or of the breast.

Markers that are differentially present in samples of cancer subjects and control subjects find application in methods and kits for determining cancer status. Accordingly, methods are provided for identifying cancer of the prostate, bladder, or breast in a subject comprising detecting a differential presence of a biomarker in subjects with cancer of the prostate, bladder, or breast vs. without cancer of the prostate, bladder, or breast in a biological sample obtained from the subject. The amount of one or more biomarkers found in a test sample compared to a control, or the presence or absence of one or more markers in the test sample provides useful information regarding the cancer status of the patient.

A. Types of Samples

The markers can be measured in different types of biological samples. The sample is preferably a biological fluid sample. Examples of a biological fluid sample useful in this invention include blood, blood serum, plasma, vaginal secretions, urine, tears, saliva, urine, tissue, cells, organs, seminal fluids, bone marrow, cerebrospinal fluid, nipple aspirate, etc. Blood serum is a preferred sample source for embodiments of the invention.

If desired, the sample can be prepared to enhance detectability of the markers. For example, to increase the detectability of markers, a blood serum sample from the subject can be preferably fractionated by, e.g., Cibacron blue agarose chromatography and single stranded DNA affinity chromatography, anion exchange chromatography, affinity chromatography (e.g., with antibodies) and the like. The method of fractionation depends on the type of detection method used. Any method that enriches for the protein of interest can be used. Typically, preparation involves fractionation of the sample and collection of fractions determined to contain the biomarkers. Methods of pre-fractionation include, for example, size exclusion chromatography, ion exchange chromatography, heparin chromatography, affinity chromatography, sequential extraction, gel electrophoresis and liquid chromatography. The analytes also may be modified prior to detection. These methods are useful to simplify the sample for further analysis. For example, it can be useful to remove high abundance proteins, such as albumin, from blood before analysis.

B. Detection of Serum Peptide Markers

Serum Peptide Marker Modification

A marker can be modified before analysis to improve its resolution or to determine its identity. For example, the markers may be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the markers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the markers, thereby enabling their detection indirectly. This is particularly useful where there are markers with similar molecular masses that might be confused for the marker in question. Also, proteolytic fragmentation is useful for high molecular weight markers because smaller markers are more easily resolved by mass spectrometry. In specific embodiments, the proteases occur or naturally exist in the biological sample.

To improve detection resolution of the markers, neuraminidase can, for instance, be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent (e.g., cationic exchange ProteinChip® arrays) and to improve detection resolution. In another example, the markers can be modified by the attachment of a tag of particular molecular weight that specifically bind to molecular markers, further distinguishing them. Optionally, after detecting such modified markers, the identity of the markers can be further determined by matching the physical and chemical characteristics of the modified markers in a protein database (e.g., SwissProt).

It has been found that proteins frequently exist in a sample in a plurality of different forms characterized by a detectably different mass. These forms can result from either, or both, of pre- and post-translational modification. Pre-translational modified forms include allelic variants, slice variants and RNA editing forms. Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cystinylation, sulphonation and acetylation. Modified forms of any marker of this invention also may be used, themselves, as biomarkers. In certain cases the modified forms may exhibit better discriminatory power in diagnosis than the specific forms set forth herein.

Serum Peptide Marker Purification

For some of the method embodiments of the invention, it may be helpful to purify the marker detected by the methods disclosed herein prior to subsequent analysis. Nearly any means known to the art for the purification and separation of small molecular weight substances, e.g., anion or cation exchange chromatography, gas chromatography, liquid chromatography or high pressure liquid chromatography may be used. Methods of selecting suitable separation and purification techniques and means of carrying them out are known in the art (see, e.g., Labadarious et. al., J. Chromatography (1984) 310:223-231, and references cited therein; and Shahrokhin and Gehrke, J. Chromatography (1968) 36:31-41, and Niessen J. Chromatography (1998) 794:407-435).

In another embodiment of the method of the invention, purification of the marker comprises fractioning a sample comprising one or more protein markers by size-exclusion chromatography and collecting a fraction that includes the one or more marker; and/or fractioning a sample comprising the one or more markers by anion exchange chromatography and collecting a fraction that includes the one or more markers. Fractionation is monitored for purity on normal phase and immobilized nickel arrays. Generating data on immobilized marker fractions on an array is accomplished by subjecting the array to laser ionization and detecting intensity of signal for mass/charge ratio; and transforming the data into computer readable form. Preferably, fractions are subjected to gel electrophoresis and correlated with data generated by mass spectrometry. In one aspect, gel bands representative of potential markers are excised and subjected to enzymatic treatment and are applied to biochip arrays for peptide mapping.

Methods of Detection

Any suitable method can be used to detect one or more of the markers described herein. Successful practice of the invention can be achieved with one or a combination of methods that can detect and, preferably, quantify the markers. These methods include, without limitation, hybridization-based methods including those employed in biochip arrays, mass spectrometry (e.g., laser desorption/ionization mass spectrometry), fluorescence (e.g. sandwich immunoassay), surface plasmon resonance, ellipsometry and atomic force microscopy. Methods may further include, by one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)^(n), matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)^(n), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)_(n), quadrupole mass spectrometry, fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero.

Biochip-Based Methods

Detection methods may include use of a biochip array. Biochip arrays useful in the invention include protein and nucleic acid arrays. One or more markers are captured on the biochip array and subjected to laser ionization to detect the molecular weight of the markers. Analysis of the markers is, for example, by molecular weight of the one or more markers against a threshold intensity that is normalized against total ion current.

The biochip surfaces may, for example, be ionic, anionic, hydrophobic; comprised of immobilized nickel or copper ions, comprised of a mixture of positive and negative ions; and/or comprised of one or more antibodies, single or double stranded nucleic acids, proteins, peptides or fragments thereof, amino acid probes, or phage display libraries. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems (Fremont, Calif.), Packard BioScience Company (Meriden Conn.), Zyomyx (Hayward, Calif.) and Phylos (Lexington, Mass.). Examples of such protein biochips are described in the following patents or patent applications: U.S. Pat. No. 6,225,047 (Hutchens and Yip, “Use of retentate chromatography to generate difference maps,” May 1, 2001); International publication WO 99/51773 (Kuimelis and Wagner, “Addressable protein arrays,” Oct. 14, 1999); U.S. Pat. No. 6,329,209 (Wagner et al., “Arrays of protein-capture agents and methods of use thereof,” Dec. 11, 2001) and International publication WO 00/56934 (Englert et al., “Continuous porous matrix arrays,” Sep. 28, 2000).

Markers may be captured with capture reagents immobilized to a solid support, such as a biochip, a multiwell microtiter plate, a resin, or nitrocellulose membranes that are subsequently probed for the presence of proteins. Capture can be on a chromatographic surface or a biospecific surface. For example, a sample containing the markers, such as serum, may be placed on the active surface of a biochip for a sufficient time to allow binding. Then, unbound molecules are washed from the surface using a suitable eluant, such as phosphate buffered saline. In general, the more stringent the eluant, the more tightly the proteins must be bound to be retained after the wash.

Upon capture on a biochip, analytes can be detected by a variety of detection methods selected from, for example, a gas phase ion spectrometry method, an optical method, an electrochemical method, atomic force microscopy and a radio frequency method. Gas phase ion spectrometry methods are described herein. Of particular interest is the use of mass spectrometry, and in particular, SELDI. Optical methods include, for example, detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry). Optical methods include microscopy (both confocal and non-confocal), imaging methods and non-imaging methods. Immunoassays in various formats (e.g., ELISA) are popular methods for detection of analytes captured on a solid phase. Electrochemical methods include voltametry and amperometry methods. Radio frequency methods include multipolar resonance spectroscopy.

Mass Spectrometry-Based Methods

Mass spectrometry (MS) is a well-known tool for analyzing chemical compounds. Thus, in one embodiment, the methods of the present invention comprise performing quantitative MS to measure the serum peptide marker. The method may be performed in an automated (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. This can be accomplished, for example with MS operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS). Methods for performing MS are known in the field and have been disclosed, for example, in US Patent Application Publication Nos: 20050023454; 20050035286; U.S. Pat. No. 5,800,979 and references disclosed therein.

The protein fragments, whether they are peptides derived from the main chain of the protein or are residues of a side-chain, are collected on the collection layer. They may then be analyzed by a spectroscopic method based on matrix-assisted laser desorption/ionization (MALDI) or electrospray ionization (ESI). The preferred procedure is MALDI with time of flight (TOF) analysis, known as MALDI-TOF MS. This involves forming a matrix on the membrane, e.g. as described in the literature, with an agent which absorbs the incident light strongly at the particular wavelength employed. The sample is excited by UV, or IR laser light into the vapour phase in the MALDI mass spectrometer. Ions are generated by the vaporization and form an ion plume. The ions are accelerated in an electric field and separated according to their time of travel along a given distance, giving a mass/charge (m/z) reading which is very accurate and sensitive. MALDI spectrometers are commercially available from PerSeptive Biosystems, Inc. (Frazingham, Mass., USA) and are described in the literature, e.g. M. Kussmann and P. Roepstorff, cited above.

Magnetic-based serum processing can be combined with traditional MALDI-TOF. Through this approach, improved peptide capture is achieved prior to matrix mixture and deposition of the sample on MALDI target plates. Accordingly, methods of peptide capture are enhanced through the use of derivatized magnetic bead based sample processing.

MALDI-TOF MS allows scanning of the fragments of many proteins at once. Thus, many proteins can be run simultaneously on a polyacrylamide gel, subjected to a method of the invention to produce an array of spots on the collecting membrane, and the array may be analyzed. Subsequently, automated output of the results is provided by using the ExPASy server, as at present used for MIDI-TOP MS and to generate the data in a form suitable for computers.

Other techniques for improving the mass accuracy and sensitivity of the MALDI-TOF MS can be used to analyze the fragments of protein obtained on the collection membrane. These include the use of delayed ion extraction, energy reflectors and ion-trap modules. In addition, post source decay and MS-MS analysis are useful to provide further structural analysis. With ESI, the sample is in the liquid phase and the analysis can be by ion-trap, TOF, single quadrupole or multi-quadrupole mass spectrometers. The use of such devices (other than a single quadrupole) allows MS-MS or MS^(n) analysis to be performed. Tandem mass spectrometry allows multiple reactions to be monitored at the same time.

Capillary infusion may be employed to introduce the marker to a desired MS implementation, for instance, because it can efficiently introduce small quantities of a sample into a mass spectrometer without destroying the vacuum. Capillary columns are routinely used to interface the ionization source of a MS with other separation techniques including gas chromatography (GC) and liquid chromatography (LC). GC and LC can serve to separate a solution into its different components prior to mass analysis. Such techniques are readily combined with MS, for instance. One variation of the technique is that high performance liquid chromatography (HPLC) can now be directly coupled to mass spectrometer for integrated sample separation/and mass spectrometer analysis.

Quadrupole mass analyzers may also be employed as needed to practice the invention. Fourier-transform ion cyclotron resonance (FTMS) can also be used for some invention embodiments. It offers high resolution and the ability of tandem MS experiments. FTMS is based on the principle of a charged particle orbiting in the presence of a magnetic field. Coupled to ESI and MALDI, FTMS offers high accuracy with errors as low as 0.001%.

In one embodiment, the marker qualification methods of the invention may further comprise identifying significant peaks from combined spectra. The methods may also further comprise searching for outlier spectra. In another embodiment, the method of the invention further comprises determining distant dependent K-nearest neighbors.

In another embodiment of the method of the invention, an ion mobility spectrometer can be used to detect and characterize serum peptide markers. The principle of ion mobility spectrometry is based on different mobility of ions. Specifically, ions of a sample produced by ionization move at different rates, due to their difference in, e.g., mass, charge, or shape, through a tube under the influence of an electric field. The ions (typically in the form of a current) are registered at the detector which can then be used to identify a marker or other substances in a sample. One advantage of ion mobility spectrometry is that it can operate at atmospheric pressure.

For the mass values of the markers disclosed herein, the mass accuracy of the spectral instrument is considered to be about within +/−0.15 percent of the disclosed molecular weight value. Additionally, to such recognized accuracy variations of the instrument, the spectral mass determination can vary within resolution limits of from about 400 to 1000 m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peak height. Mass accuracy and resolution variances and thus meaning of the term “about” with respect to the mass of each of the markers described herein is inclusive of variants of the markers as may exist due to sex, genotype and/or ethnicity of the subject and the particular cancer or origin or stage thereof.

In an additional embodiment of the methods of the present invention, multiple markers are measured. The use of multiple markers increases the predictive value of the test and provides greater utility in diagnosis, toxicology, patient stratification and patient monitoring. The process called “Pattern recognition” detects the patterns formed by multiple markers greatly improves the sensitivity and specificity of clinical proteomics for predictive medicine. Subtle variations in data from clinical samples indicate that certain patterns of protein expression can predict phenotypes such as the presence or absence of a certain disease, a particular stage of cancer-progression, or a positive or adverse response to drug treatments.

C. Data Analysis

Data generated by desorption and detection of markers can be analyzed using any suitable means. In one embodiment, data is analyzed and/or stored by electronic means, such as with the use of a programmable digital computer. The computer program generally contains a readable medium that stores codes. Certain code can be devoted to memory that includes the location of each feature on a probe, the identity of the adsorbent at that feature and the elution conditions used to wash the adsorbent. The computer also contains code that receives as input, data on the strength of the signal at various molecular masses received from a particular addressable location on the probe. This data can indicate the number of markers detected, including the strength of the signal generated by each marker.

Data analysis can include the steps of determining signal strength (e.g., height of peaks) of a marker detected and removing “outliers” (data deviating from a predetermined statistical distribution). The observed peaks can be normalized, a process whereby the height of each peak relative to some reference is calculated. For example, a reference can be background noise generated by instrument and chemicals (e.g., energy absorbing molecule) which is set as zero in the scale. Then the signal strength detected for each marker or other biomolecules can be displayed in the form of relative intensities in the scale desired (e.g., 100). Alternatively, a standard (e.g., a serum protein) may be admitted with the sample so that a peak from the standard can be used as a reference to calculate relative intensities of the signals observed for each marker or other markers detected.

The computer can transform the resulting data into various formats for displaying, such as “spectrum view or retentate map,” “peak map,” “gel view,” “3-D overlays,” “difference map view,” and Spotfire Scatter Plot. For each sample, markers that are detected and the amount of markers present in the sample can be saved in a computer readable medium. This data can then be compared to a control (e.g., a profile or quantity of markers detected in control, e.g., subjects in whom human cancer is undetectable).

When the sample is measured and data is generated, e.g., by mass spectrometry, the data is then analyzed by a computer software program. Generally, the software can comprise code that converts signal from the mass spectrometer into computer readable form. The software also can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a “peak” in the signal corresponding to a marker of this invention, or other useful markers. The software also can include code that executes an algorithm that compares signal from a test sample to a typical signal characteristic of “normal” and human cancer and determines the closeness of fit between the two signals. The software also can include code indicating which the test sample is closest to, thereby providing a probable diagnosis.

TOF-to-M/Z transformation involves the application of an algorithm that transforms times-of-flight into mass-to-charge ratio (M/Z). In this step, the signals are converted from the time domain to the mass domain. That is, each time-of-flight is converted into mass-to-charge ratio, or M/Z. Calibration can be done internally or externally. In internal calibration, the sample analyzed contains one or more analytes of known M/Z. Signal peaks at times-of-flight representing these massed analytes are assigned the known M/Z. Based on these assigned M/Z ratios, parameters are calculated for a mathematical function that converts times-of-flight to M/Z. In external calibration, a function that converts times-of-flight to M/Z, such as one created by prior internal calibration, is applied to a time-of-flight spectrum without the use of internal calibrants.

Baseline subtraction improves data quantification by eliminating artificial, reproducible instrument offsets that perturb the spectrum. It involves calculating a spectrum baseline using an algorithm that incorporates parameters such as peak width, and then subtracting the baseline from the mass spectrum.

High frequency noise signals are eliminated by the application of a smoothing function. A typical smoothing function applies a moving average function to each time-dependent bin. In an improved version, the moving average filter is a variable width digital filter in which the bandwidth of the filter varies as a function of, e.g., peak bandwidth, generally becoming broader with increased time-of-flight. See, e.g., WO 00/70648, Nov. 23, 2000 (Gavin et al., “Variable Width Digital Filter for Time-of-flight Mass Spectrometry”).

As mentioned briefly above, analysis generally involves the identification of peaks in the spectrum that represent signal from an analyte. Peak data from one or more spectra can be subject to further analysis by, for example, creating a spreadsheet in which each row represents a particular mass spectrum, each column represents a peak in the spectra defined by mass, and each cell includes the intensity of the peak in that particular spectrum. Various statistical or pattern recognition approaches can applied to the data.

The spectra that are generated in embodiments of the invention can be classified using a pattern recognition process that uses a classification model. In some embodiments, data derived from the spectra (e.g., mass spectra or time-of-flight spectra) that are generated using samples such as “known samples” can then be used to “train” a classification model. A “known sample” is a sample that is pre-classified (e.g., cancer or not cancer). The data that are derived from the spectra and are used to form the classification model can be referred to as a “training data set”. Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased vs. non diseased).

The classification models can be formed on and used on any suitable digital computer. The digital computer that is used may be physically separate from the mass spectrometer that is used to create the spectra of interest, or it may be coupled to the mass spectrometer.

MALDI-TOF MS-Based Quantitative Profiling

Relative quantitation of serum peptides of interest can be done by comparing the MS-ion intensities to those of added, exogenous, isotopically labeled, reference peptides, having the exact same sequence and otherwise same chemical properties as the endogenous ones (i.e. distinguishable by molecular mass only). As such, all peptide pairs will display the exact same MALD-ionization characteristics. Comparing ion intensities will therefore provide a means of normalizing the values for each peptide. For instance, when the ion intensity of peptide A is two-fold higher than the spiked reference in sample X but two-fold lower in sample Y, then the difference would be about 4-fold between the same peptide in the two samples. When done on a systematic, larger scale, this approach can be referred to as relative “quantitative” profiling. Of note, the reference peptides will be added to the raw serum (i.e., before peptide extraction and MALDI sample prep), so that putative losses during processing are accounted for.

66 reference peptides (listed in FIG. 31) can be synthesized, 44 of which have been determined to be surrogate markers for either prostate or breast cancer, 18 additional ones for bladder or thyroid cancer, and 4 non-marker control peptides. These reference peptides should not degrade in serum, and are, thus, synthesized using D-amino acids (i.e., D-stereo-isomers). One amino acid (Leu, Val, or Phe) of each reference peptide is labeled by incorporation of 6 (L, F) or 5 (V)¹³C isotopes, and one additional ¹⁵N isotope (V only). ¹³C-labeled, FMOC-amino acids (for solid phase-peptide synthesis) are only commercially available in the L-form, which should not compromise stability as peptide bonds between a D- and L-amino acids are not protease sensitive.

MALDI-TOF MS-Based Protease Assays

A large part of the human serum ‘peptidome’, as detected by MALDI-TOF MS, is generated ex vivo (i.e., after blood collection) by protease degradation of blood proteins. Endoproteases produce ‘founder peptides’ which are then pared down by exoproteases into ladder-like clusters. Panels of proteolytic activity in the blood contribute important cancer type-specific information, and that the resulting metabolic patterns have utility as surrogate markers for detection and classification of cancer. Degradation occurs during clotting. The use of exogenous synthetic peptides, identical to previously observed founder peptides, can be used to monitor cancer-specific proteolytic degradation in plasma or serum that contains proteases. Conditions in terms of time, temperature and added amounts of substrates can hereby be readily controlled. Coupled to a MALDI-based read-out, such analyses are blood “protease assays” to monitor the tumor-dependent activities inferred from prior studies. Simultaneous addition of non-degradable, exogenous reference peptides also enables relative quantitation of all rungs in the ladders.

Exogenous peptide degradation assays can be done, for example, in plasma, where there are no endogenous peptides that clutter the spectra, therefore simplifying interpretation. Thus, in addition to serving as (i) an alternative to endogenous serum peptide profiling, and as (ii) a highly reproducible, functional proteomics approach, the external peptide degradation assay (iii) permit analysis of plasma by the NY consortium, which is important as plasma is preferred by many for proteomic studies.

15 founder peptides (listed in FIG. 33) can be synthesized, all ‘double-isotopically’ labeled to be 12 Da heavier in molecular mass than their endogenous counterparts and 6 Da heavier than the non-degradable reference peptides. Selection is based on a sequence comparison of all previously observed peptide ladders in serum, most of which contain some known surrogate marker peptides. Synthesis, QC, quantitation and storage of the peptides will be done as described previously.

The degradation conditions and times are studied and optimized for each of the 15 synthetic founder peptides in each of the plasmas from the different groups of cancer patients and controls. The permissible inter-mixability of the different founders, and, particularly, of their resulting degradation ladders is determined in order to avoid disturbing the peak patterns (by ion suppression effects) and to avoid overlapping isotopic envelopes (when the peaks are too close).

As aminopeptidases come in varieties that remove one two or three amino acids, shorter endogenous peptides may have conceivably been derived from another precursor by leapfrogging over the stalled position. For non-degradable “founder” peptides, limited N-terminal ladders can be synthesized (by sequential sampling of resin during a pilot scale synthesis of unlabeled peptides), for instance, as shown in FIG. 33 (founder #7; five alternative ‘test’ founder peptides), and degradability can be tested in pooled cancer patient plasma in a time course (15 min to 4 hours) experiment. Similar tests are performed for founder peptides 8, 9, 10 and 12A in FIG. 33. Each time, synthesis is carried out of the “full-length” founder, but resin sampled at 5, 4, 3, 2 and 1 amino acid away from the N-terminus, or as appropriate. The longest peptide is cleaved from the resin, purified, and tested. If no degradation in plasma is observed, the shorter versions are also cleaved, purified and tested. An isotope-labeled version of the peptide with the best founder properties (i.e., generating the best ladder in plasma) is then produced.

The assay may be divided into ‘founder pools’ if two or more time points are too far apart or in the case of peptide inter-mixability problems. Once the ideal conditions and founder pools have been selected, and the resulting degradation products are identified, a relative quantitation aspect can be added to the blood protease assay by using the same non-degradable reference peptides as shown in FIG. 31.

D. Diagnosis

As indicated above, the invention provides methods for aiding a human cancer diagnosis using one or more markers, as specified herein. These markers can be used alone, in combination with other markers in any set, or with entirely different markers in aiding human cancer diagnosis. The markers are differentially present in samples of a human cancer patient and a normal subject in whom human cancer is undetectable. For example, some of the markers are expressed at an elevated level and/or are present at a higher frequency in human prostate cancer subjects than in normal subjects, while some of the markers are expressed at a decreased level and/or are present at a lower frequency in human prostate cancer subjects than in normal subjects. Therefore, detection of one or more of these markers in a person would provide useful information regarding the probability that the person may have prostate cancer.

The detection of the peptide marker is then correlated with a probable diagnosis of cancer. In some embodiments, the detection of the mere presence or absence of a marker, without quantifying the amount thereof, is useful and can be correlated with a probable diagnosis of cancer. The measurement of markers may also involve quantifying the markers to correlate the detection of markers with a probable diagnosis of cancer. Thus, if the amount of the markers detected in a subject being tested is different compared to a control amount (i.e., higher or lower than the control, depending on the marker), then the subject being tested has a higher probability of having cancer.

The correlation may take into account the amount of the marker or markers in the sample compared to a control amount of the marker or markers (up or down regulation of the marker or markers) (e.g., in normal subjects or in non-cancer subjects such as where cancer is undetectable). A control can be, e.g., the average or median amount of marker present in comparable samples of normal subjects in normal subjects or in non-cancer subjects such as where cancer is undetectable. The control amount is measured under the same or substantially similar experimental conditions as in measuring the test amount. As a result, the control can be employed as a reference standard, where the normal (non-cancer) phenotype is known, and each result can be compared to that standard, rather than re-running a control.

Accordingly, a marker profile may be obtained from a subject sample and compared to a reference marker profile obtained from a reference population, so that it is possible to classify the subject as belonging to or not belonging to the reference population. The correlation may take into account the presence or absence of the markers in a test sample and the frequency of detection of the same markers in a control. The correlation may take into account both of such factors to facilitate determination of cancer status.

In certain embodiments of the methods of qualifying cancer status, the methods further comprise managing subject treatment based on the status. The invention also provides for such methods where the markers (or specific combination of markers) are measured again after subject management. In these cases, the methods are used to monitor the status of the cancer, e.g., response to cancer treatment, remission of the disease or progression of the disease.

The markers of the present invention have a number of other uses. For example, they can be used to monitor responses to certain treatments of human cancer. In yet another example, the markers can be used in heredity studies. For instance, certain markers may be genetically linked. This can be determined by, e.g., analyzing samples from a population of human cancer subjects whose families have a history of cancer. The results can then be compared with data obtained from, e.g., cancer subjects whose families do not have a history of cancer. The markers that are genetically linked may be used as a tool to determine if a subject whose family has a history of cancer is pre-disposed to having cancer.

Any marker, individually, is useful in aiding in the determination of cancer status. First, the selected marker is detected in a subject sample using the methods described herein (e.g. mass spectrometry). Then, the result is compared with a control that distinguishes cancer status from non-cancer status. As is well understood in the art, the techniques can be adjusted to increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician.

While individual markers are useful diagnostic markers, in some instances, a combination of markers provides greater predictive value than single markers alone. The detection of a plurality of markers (or absence thereof, as the case may be) in a sample can increase the percentage of true positive and true negative diagnoses and decrease the percentage of false positive or false negative diagnoses. Thus, preferred methods of the present invention comprise the measurement of more than one marker.

E. Kits

In one aspect, the invention provides kits for monitoring and diagnosing cancer, wherein the kits can be used to detect the markers described herein. For example, the kits can be used to detect any one or more of the markers potentially differentially present in samples of cancer subjects vs. normal subjects. The kits of the invention have many applications. For example, the kits can be used to differentiate if a subject has cancer or has a negative diagnosis, thus aiding a cancer diagnosis. In another embodiment, the invention provides kits for aiding the diagnosis of cancer or the diagnosis of a specific type of cancer such as, for example, cancer of the prostate, of the bladder, or of the breast. The kits can also be used to identify compounds that modulate expression of one or more of the herein-described markers in in vitro or in vivo animal models for cancer.

In specific embodiments, kits of the invention contain an exogenous reference peptide, which is optionally isotopically labeled, for use in conducting the diagnostic assays of the invention.

The kits of the invention may include instructions for the assay, reagents, testing equipment (test tubes, reaction vessels, needles, syringes, etc.), standards for calibrating the assay, and/or equipment provided or used to conduct the assay. Reagents may include acids, bases, oxidizing agents, marker species. The instructions provided in a kit according to the invention may be directed to suitable operational parameters in the form of a label or a separate insert.

The kits may also include an adsorbent, wherein the adsorbent retains one or more markers selected from one or more of the markers described herein, and written instructions for use of the kit for detection of cancer. Such a kit could, for example, comprise: (a) a substrate comprising an adsorbent thereon, wherein the adsorbent is suitable for binding a marker, and (b) instructions to detect the marker or markers by contacting a sample with the adsorbent and detecting the marker or markers retained by the adsorbent. Accordingly, the kit could comprise (a) a DNA probe that specifically binds to a marker; and (b) a detection reagent. Such a kit could further comprise an eluant (as an alternative or in combination with instructions) or instructions for making an eluant, wherein the combination of the adsorbent and the eluant allows detection of the markers using gas phase ion spectrometry.

Optionally, the kit may further comprise a standard or control information so that the test sample can be compared with the control information standard to determine if the test amount of a marker detected in a sample is a diagnostic amount consistent with a diagnosis of cancer.

This invention is further illustrated by the following examples, which should not be construed as limiting. A skilled artisan should readily understand that other similar instruments with equivalent function/specification, either commercially available or user modified, are suitable for practicing the instant invention. Rather, the invention should be construed to include any and all applications provided herein and all equivalent variations within the skill of the ordinary artisan.

EXAMPLES Example 1 Unsupervised Hierarchical Clustering and PCA of Mass Spectrometry-Based Serum Peptide Profiling Data

In order to determine if selected patterns of serum peptides with known sequences can (i) separate cancer from non-cancer, (ii) distinguish between different types of solid tumors, and (iii) allow class prediction with an independent validation set, the serum peptide profiles were analyzed from patients with advanced prostate, breast, or bladder cancer, as well as control sera from healthy volunteers, all collected using a standardized protocol (Villanueva, J., et al., 2005. J. Proteome Res: 4:1060-1072).

A. Methods

Serum Samples

Blood samples from n=33 healthy volunteers (mixed gender; ages 23 to 49) with no known malignancies and from patients diagnosed with advanced prostate cancer (n=32), bladder cancer (n=20), or breast cancer (n=21) were collected following a standard clinical protocol (Villanueva, J., et al., 2005. J Proteome Res: 4:1060-1072) and approved by the MSKCC Institutional Review and Privacy Board. Blood samples were obtained in 8.5-mL, BD Vacutainer, glass ‘red-top’ tubes (Becton Dickinson #366430, Franklin Lakes, N.J.), allowed to clot at room temperature for 1 hour, and centrifuged at 1400-2000 RCF for 10 min, at RT.

Sera (upper phase) were transferred to four 4-mL cryovials (Fisher #0566966), ˜1 mL serum in each, and stored frozen at −80° C. until further use (Villanueva, J., et al. 2005. J Proteome Res: 4:1060-1072). A similar procedure was followed for preparation of plasma in heparin-containing ‘green-top’ tubes (BD #366480), except that centrifugation was done immediately after blood collection. Upon delivery at the mass spectrometry (MS) laboratory, the cryovials (source vials) were barcoded. One cryovial of each sample was thawed on ice and used to generate nine smaller aliquots (50 μL each) in barcoded micro-eppendorf tubes and stored at −80° C. in barcoded freezer boxes. In the present study, every serum sample underwent two freeze/thaw cycles, the second thawing step occurring immediately prior to peptide extraction and MS analysis.

All 106 serum samples were processed automatically as a single batch with a robot liquid handler followed within one hour by automated MALDI-TOF mass spectrometric analysis. The four clinical groups were randomized before automated solid-phase peptide extraction and MALDI-TOF mass spectrometry.

Automated, Solid-Phase Peptide Extraction

Serum peptide profiling was accomplished using a technology platform developed for simultaneous measurement of large numbers of serum polypeptides (Villanueva, J., et al. 2004. Anal Chem 76:1560-1570). It uses magnetic bead-based, solid-phase extraction of predominantly small peptides followed by a MALDI-TOF MS read-out. The system is intrinsically more sensitive than any surface capture on chips, as spherical particles have larger combined surface areas than small-diameter spots. When combined with high-resolution MS, hundreds of peptides are detected in a single droplet of serum.

For the present analysis, peptides were captured and concentrated using SiMAG-C8/K superparamagnetic, silica-based particles (≦1 micron diameter; 80% iron oxide; non-porous), bearing C8 reversed-phase (RP) ligands (Chemicell, Berlin, Germany). All analyses were performed in a 96-well format, using the same batch of C8 magnetic particles, in 0.2-mL polypropylene tubes (8×12-tube ‘Temp Plate II’; USA Scientific, Ocala, Fla.).

The protocol is based on a detailed investigation of serum handling, RP ligand and eluant selection (Villanueva, J., et al. 2004. Anal Chem 76:1560-1570), and is automated using a ‘Genesis Freedom 100’ (Tecan; Research Triangle Park, N.C.) liquid handling workstation for throughput and reproducibility. The system was programmed either directly via its standard software or, when individual wells needed to be accessed independently, indirectly through its work-lister capability. This system automates all of the liquid-handling steps, including magnetic separation via a robotic manipulating arm, mixing of eluates with MALDI matrix and deposition onto the Bruker 384-spot MALDI target plates. A computer randomization program was used to position case and control samples for both solid-phase extraction and mass spectrometry.

Mass Spectrometry

Peptide profiles were analyzed with an Autoflex MALDI-TOF mass spectrometer (Bruker; Bremen, Germany) equipped with a 337 nm nitrogen laser, a gridless ion source, delayed-extraction (DE) electronics, a high-resolution timed ion selector (TIS), and a 2 GHz digitizer. Separate spectra were obtained for two restricted mass-to-charge (m/z) ranges, corresponding to polypeptides with molecular mass of 0.7-4 kDa (“≦4 kD”) and 4-15 kDa (“≧4 kD”) (assuming z=1), under specifically optimized instrument settings. Each spectrum was the result of 400 laser shots, per m/z segment per sample, delivered in four sets of 100 shots (at 50-Hz frequency) to each of four different locations on the surface of the matrix spot.

The peak list (normalized intensities of 651 m/z-peaks, i.e., peptide-ions, in all 106 samples) generated was subjected to a Mann-Whitney U test, for each of the cancer groups individually versus the control. In a first selection, 196 peaks with adjusted p-values <0.00001 (arbitrarily chosen) for at least one cancer type were retained. This number was reduced to 68 by applying an arbitrary threshold (500 ‘units’) to the median intensities of each individual peptide peak within a group. An m/z-peak was selected if it passed the threshold in at least one of the cancer groups or the control (FIG. 13).

A weekly performance test was carried out with commercial human reference serum (# S-7023, lot 034K8937; Sigma, St Louis, Mo.), and the effective laser energy delivered to the target was adjusted when necessary. The entire irradiation program was automated using the instrument's ‘AutoXecute’ function. Spectra were acquired in linear mode geometry under 20 kV (18.6 kV during DE) of ion accelerating and −1.3 kV multiplier potentials, and with gating of mass ions ≦400 m/z (≦4 kD segment) or ≦3,000 m/z (≧4 kD segment). DE was maintained for 80 (≦4 kD) or 50 nanoseconds (≧4 kD) to give appropriate time-lag focusing after each laser shot.

Peptide samples were consistently mixed with two volumes of pre-made a-cyano-4-hydroxycinnamic acid (ACCA) matrix solution (Agilent; Palo Alto, Calif.), deposited onto the stainless steel target surface, in every other column of the 384-spot layout, and allowed to dry at room temperature. Thirty fmoles (per peptide) and 500 fmoles (per protein) of commercially available calibration standards (Bruker Daltonics #206195 (<4 kD) and #206355 (>4 kD)) were also mixed with ACCA matrix and separately deposited onto the target plates, adjacent to each spotted serum sample (one sample/one standard), in the alternating columns. All spectra were acquired within less than 1-2 hours after completion of robotic sample processing, as an adverse effect had previously been observed upon increasing times between crystallization and mass spectral acquisition.

The AutoFlex MALDI-TOF has a probe at the output of the laser, before the attenuator. The accuracy of this monitoring device was verified prior to the calibration of the settings of the attenuator (displayed on the computer screen as an arbitrary scale of 100-0%) by measuring transmitted energy at varying %. This allowed the generation of a calibration curve to convert before-to-after attenuation laser energy. The optimal laser setting that had been empirically determined was then measured to yield 16-μJ energy per pulse, post-attenuation. Laser output energy was measured and documented on a weekly basis, and adjustments were made accordingly to compensate for fading laser energy over time.

Samples from patients with different cancers and from controls were randomly distributed during processing and analysis.

Signal Processing

Once acquired, all data were stored with a naming convention that allows each sample to be associated with its calibrant. The spectra were first converted from binary format to ASCII files containing two columns of data (x: m/z, y: intensity) by a custom written macro in FlexAnalysis (Bruker). For the lower mass range (700-4,000 Da), about 48,000 x,y-points were generated, while for the upper mass range (4-15 kDa), there were about 77,000 points.

Further data processing was carried out in MATLAB with a custom script called ‘Qcealign’ using only the ASCII versions of the raw spectra. ‘Qcealign’ used the ‘Qpeaks’ program (Spectrum Square Associates, Ithaca, N.Y.) for smoothing, baseline subtraction and peak labeling. The singletwidth parameter required by ‘Qpeaks’ was set to −400 for the lower mass range and −200 for the upper mass range, thereby specifying the resolution, (m/z)/Δ(m/z), for processing. This peak information was used automatically by ‘Qpeaks’ in setting the parameters for smoothing, baseline-subtraction, and binning. The noise statistics were assumed ‘Normal’.

Following parameter selection, a setup file was created. ‘Qcealign’ then queries the setup file to obtain a list of all the directories for processing. During a single processing run, all data files in all listed directories are aligned with each other. For each directory, singletwidth information is provided in the setup file, along with parameters controlling calibration, peak labeling sensitivity, alignment, etc. The files containing the polypeptide standards are calibrated first. The centroid positions of peaks in these calibration files are obtained from the peak table created by ‘Qpeaks’, compared to the known polypeptide peak positions, and a quadratic calibration equation for correcting the measured masses in each calibration file is created. The calibration equations are saved to disk for use in calibrating the mass axes of the sample files.

‘Qcealign’ subsequently creates a reference file to which all sample spectra will later be aligned. The first data file is loaded and calibrated by applying the curve calculated from its associated calibrant spectrum. This file's x-axis (m/z) becomes the x-axis (and thus the calibration) used in the reference file. ‘Qcealign’ then loads all other sample files, calibrates them, and adds their intensities to the reference file's intensity. After all samples have been added, the reference spectrum becomes the average of all the sample files. The reference is processed with ‘Qpeaks’ to find a baseline, which is subtracted, and is then normalized to unit size by dividing each intensity value by the Total Ion Count (TIC). Once normalized, a scaling factor is added by multiplying each intensity value by a user-selected number (e.g., 10⁷). This scaling factor is constant within a data set and is used to convert the normalized spectrum to a “user friendly” scale, where most peak heights are greater than one. Next, ‘Qcealign’ processes each sample file with ‘Qpeaks’ to create a peak table, smoothed curve and a baseline. This spectrum is then taken for alignment.

Alignment

Processed spectra were aligned using the custom ‘Entropycal’ program described herein above. A custom alignment algorithm, ‘Entropycal’, aligns sample data files to a reference file using a minimum entropy algorithm by taking unsmoothed (‘raw’), baseline-corrected data. Taking raw spectra for alignment facilitates the use of all statistical information in the data; processed data contains less information. The alignment is performed in two steps: ‘Entropycal’ and binning. ‘Entropycal’ slides each data file by ‘n’ data points to the right or left along the x-axis of the reference file. At each relative position n, the Shannon entropy of the sum of the two files is computed. The optimal alignment occurs at the shift that produces the minimum Shannon entropy. Second, the aligned peak lists are binned by using the resolution of the peaks: all peaks in rows within Δ(m/z) of the strongest peak at a given value of m/z are binned together, and a spreadsheet is created for further statistical analysis.

Three software modules, developed in MATLAB, were used for visualization and signal processing of the spectra. (I) Signal Processing & Preview (SPP), a graphical viewer for spectra in ASCII format, allows to plot raw and processed spectra side-by-side to review the outcome of signal processing. Furthermore, parameters of ‘Qpeaks’ (the signal processing software) can be adjusted. (II) Mass Spectra Viewer (MSV), a visual interface for processed spectral data, plots spectra as X-Y curves (mass vs. magnitude) for examining the signatures of several groups of samples. MSV supports regular browsing functions such as scroll, zoom, highlighting, etc. (III) HeatMap (HM) displays spectra as a 2D heat map images, in which the magnitude of the peaks are color-coded on a continuous scale. In addition to browsing functions such as zoom and scroll, the rank of X- and Y-position coordinates can be reorganized without the constraints of statistical correlation that are enforced by most HeatMap commercial software packages.

Ratios were calculated by dividing the median normalized intensity of each m/z-peak in each cancer group by the median of the same m/z-peak in the control group. To avoid having to divide by zero, any median value of less than was converted to 1; this was applied to all groups. For hierarchical clustering, the 651 m/z-values were subjected to average-linkage hierarchical clustering analysis using the available algorithm in ‘GeneSpring’. The peaks were organized by creating mock-phylogenetic trees (dendrograms) termed ‘gene trees’ and ‘experiment tree’ in the software. The trees were displayed with the samples along the X-axis and the masses along the Y-axis. The clustering method for both trees also measured similarity by Standard Correlation (also known as ‘Pearson correlation around zero’) as the distance matrix.

A spreadsheet (‘peak list’), containing the normalized intensities of all 651 peaks for each of the samples was taken for unsupervised, average-linkage hierarchical clustering using standard correlation. This resulted in a high degree of separation between each of the cancer types and the controls in either binary or multi-class comparisons (FIGS. 1B and 1C). Recognizing that correlations between patient samples involving 651 features would be difficult at different times and locations, statistical feature selection was performed to identify the most discriminant peaks.

The binned spreadsheet, containing data from spectra obtained for all samples of cancer patients or healthy subjects (106 samples total; 651 m/z values, with normalized intensities for each sample; >70,000 data points), as well as the test set for prostate (‘Prostate #2’; 41 samples; ˜27,000 data points), were imported into the ‘GeneSpring’ program (Agilent; Palo Alto, Calif.) and analyzed using various statistical algorithms, such as one-way ANOVA, PCA, hierarchical clustering, K-NN and SVM.

Different “experiments” were created in ‘GeneSpring’ to represent the masses. No normalizations were applied to the experiment, since the masses were normalized by the database that binned them. In the parameter section of the experiments, a parameter called ‘cancertype’ was created to label samples as prostate cancer, breast cancer, bladder cancer, or control. In the Experiment's Interpretation section, the Analysis mode was set to “Ratio (signal/control)”, and all measurements were used. No Cross-Gene Error model was used.

For ANOVA, once the experiments were created, the m/z-values (‘peaks’) were filtered by using non-parametric tests: Mann-Whitney test (for binary comparisons) and Kluskal-Wallis test (for multi-class comparisons) with Benjamini and Hochberg False Discovery Rate at p<1e-5. These tests are meant to find peaks that show statistically significant differences between the clinical groups studied.

For class prediction, K-nearest-neighbor (K-NN) analysis and Support Vector Machine (SVM) were carried out by using the Class Prediction Tool in ‘GeneSpring’. The training groups constituted either a binary comparison (prostate #1 and Control) or a multi-class comparison (prostate #1, breast, bladder and control). The test set was ‘prostate #2’. The Parameter to Predict was set to Cancertype. The Gene selection was set to use different groups of masses previously selected (e.g., 651, 68, 14, 13). In K-NN, the number of neighbors was set to five with a p-value decision cutoff of 1. The SVM was done with the same training sets and parameters and set to predict the Prostate #2 test set. The kernel used was polynomial dot product (Order 1) with a diagonal scaling of 0.

B. Results

1. Distribution of serum peptides, detected by MALDI-TOF MS, as a function of mass-to-charge (m/z) range and normalized intensity.

Peptides were extracted from 106 different serum samples (50 μL), drawn from one of three groups of cancer patients or healthy controls, analyzed by MALDI-TOF MS and the m/z-peaks were exported from the aligned spectra, as described earlier. In FIG. 11A, a total of 651 unique m/z-peaks, i.e., peptide-ions, derived from the combined spectra, are grouped in successive bins of 250 amu, starting at m/z=700.

In FIG. 11B, all peak intensities of all samples (i.e., 651×106 peaks) are grouped in successive bins of 100 arbitrary units, starting at zero. The intensities refer to normalized units that were calculated for each peak by dividing its raw intensity by the total of all the intensities in that spectrum (TIC—Total Ion Count). The resultant values were then multiplied by fixed scaling factor (1×10⁷) to convert the data to a ‘user-friendly’ scale (i.e. most values ≧1) Serum peptide profiling resulted in a total of 651 distinct mass/charge (m/z) values resolved in the 800-15,000 Dalton range (FIG. 16A).

2. Serum peptides, determined by MALDI-OF MS, before and after two successive feature selection steps for candidate markers.

One-way ANOVA Mann-Whitney test, for each individual cancer versus control, selected 196 peaks (red bars, FIG. 12) with a false positive rate of p<0.00001 (arbitrarily chosen) for at least one cancer type. This number was further reduced to 68 (yellow bars, FIG. 12) by applying an arbitrary threshold of 500 ‘units’ to the median intensities of each individual peptide peak within a group. The threshold was set high enough to select only robust peaks in the spectra, with intensities that would permit MALDI MS/MS-based tandem mass spectrometric sequencing and to exclude closely positioned neighboring peaks or ‘shoulders’.

An m/z-peak was selected if this criterion was met in at least one of the cancer groups or the control (FIG. 13). When feature selection was repeated using a multi-class Kluskal-Wallis test (adjusted p<1e-5) and the same median intensity threshold as above, 214 and 67 peaks were selected (data not shown). The majority of selected peaks corresponded to peptides with molecular mass <2,000 Da; most peptides with a mass >4,000 Da were removed (FIG. 2A; FIG. 13). Thus, significance levels (p-values) were calculated for each m/z-peak using the Mann-Whitney rank sum test (for binary comparisons) or the Kruskal-Wallis test (for multi-class comparisons) (FIG. 16B).

Example 2 Feature Selection and Comparative Analysis of Serum Peptide Profiling Data

Feature Selection

The peak list (normalized intensities of 651 m/z-peaks in all 106 samples), generated as described in Example 1, above, was subjected to one-way ANOVA Mann-Whitney test for each of the three previously identified cancer groups individually vs. the control. For each of the three cancer groups versus the control, 196 peaks with a p-value <1e-5 were arbitrarily selected and retained (FIG. 12). This number was subsequently reduced to 68 by applying an arbitrary threshold (500 ‘units’) to the median intensities of each individual peptide peak within a group. The threshold was set high enough to only select robust peaks in the spectra, with intensities that would permit MALDI TOF/TOF-based tandem mass spectrometric sequencing and to exclude closely positioned neighboring peaks or ‘shoulders’. An m/z peak was selected if it passed the threshold in at least one of the cancer groups or the control.

The pie-charts depicted in FIG. 2A illustrate the effect of using a significance level (p<0.00001) cutoff by itself, or in combination with a cutoff for the median of normalized intensities (≧500) within any one group, on the m/z distribution of the candidate biomarker peptides. After the first filter, the 196 remaining peptides were redistributed in groups of 92, 76 and 28 for the increasing mass ranges. Sixty eight peptides passed the second filter; 39, 22 and merely 7 in the low-, medium- and high-mass ranges, respectively (right panel, FIG. 2A).

Examples are shown in FIGS. 2D and 3. The majority of the selected peaks corresponded to peptides with molecular mass <2,000 Da; most peptides with a mass >4,000 Da were eliminated (FIGS. 12 and 2A). Color-coded spectra from all samples were subsequently overlaid to visually inspect the 68 peaks for correct assignment, degree of separation, and overall difference between cancer and control. Of the peptides that passed the above-delineated two selection steps, 47 m/z peaks had higher intensities in one or more of the cancer groups, as compared to the controls, and 23 had lower intensities, as compared with the control. Of those, two were higher in breast cancer but lower in bladder cancer.

The total numbers of peptides of a specific cancer group that were observed to be up or down (have specific biomarker potential) were as follows: 3 peptides were up and 11 down (14 total—1 unique, 3 shared) in serum samples from prostate cancer patients, 12 up/2 down (14 total—11 unique) in breast cancer, and 36 up/22 down (58 total—43 unique) in bladder cancer (FIG. 2B).

Comparative analysis via heat map display and mass spectral overlay: Comparison of the selected features (Tables 17A-C) of the three cancer groups with controls in multi-class and binary formats was accomplished with heat maps. Heat map displays were generated using a MATLAB custom software tool.

Three software modules, developed in MATLAB, were used for visualization and signal processing of the spectra. (I) Signal Processing & Preview (SPP), a graphical viewer for spectra in ASCII format, allows the plotting of raw and processed spectra side-by-side to review the outcome of signal processing. Furthermore, parameters of ‘Qpeaks’ (the signal processing software) can be adjusted. (II) Mass Spectra Viewer (MSV), a visual interface for processed spectral data, plots spectra as X-Y curves (mass vs. magnitude) for examining the signatures of several groups of samples. MSV supports regular browsing functions such as scroll, zoom, highlighting, etc. (III) HeatMap (HM) displays spectra as 2D heat map images, in which the magnitude of the peaks are color-coded on a continuous scale. In addition to browsing functions such as zoom and scroll, the rank of X- and Y-position coordinates can be reorganized without the constraints of statistical correlation that are enforced by most HeatMap commercial software packages.

The results, when represented in the form of heat maps in FIG. 2C, indicated that data reduction (by ˜90%) did not adversely affect the separation of the clinical groups.

Subsequently, mass spectra for the three binary comparisons (cancer vs. control) were processed as described earlier and displayed using Mass Spectra Viewer (MSV) (FIG. 2C).

Example 3 Serum Peptide Barcodes for Advanced Prostate, Bladder, and Breast Cancer

A. Methods

Assigning Peptide Sequences

A set of peptides previously selected on the basis of statistical differences in intensity between cancers and control groups was analyzed by MALDI-TOF/TOF tandem mass spectrometry, using an UltraFlex TOF/TOF instrument (Bruker; Bremen, Germany) operated in ‘LIFT’ mode. The mono-isotopic masses were first assigned by one-dimensional reflectron-TOF MS, in the presence of three peptide calibrants (6 fmoles each; calculated monoisotopic masses of 2,108.155 Da, 1,307.762 Da and 969.575 Da in the protonated form), as previously described (Winkler, G. S., et al; 2002, Methods 26:260-269).

Spectra were obtained by averaging multiple signals; laser irradiance and number of acquisitions (typically 100-150) were operator-adjusted to yield maximal peak deflections derived from the digitizer in real time. Mono-isotopic masses were assigned for all selected and other prominent peaks after visual inspection, and the low- and high-end internal standards were used for recalibration. The pass/fail criterion for recalibration is a correct assignment of an m/z value for the ‘middle’ calibrant with a mass accuracy equal or better than 12 ppm.

Alternatively, a QSTAR XL Hybrid quadrupole (Q) time-of-flight mass spectrometer (Applied Biosystems/MDS Sciex; Concord, Canada), equipped with an o-MALDI ion source, was used for both duplicate and additional tandem-MS analyses. By selecting precursor ions of interest in ‘Q1’ (operated in the mass-filter mode), mass measurements of fragment ions could be obtained in the TOF detector following collision-induced dissociation (CID) in ‘Q2’. Typically, a mass window of 3 Da was selected in order to transmit the entire isotopic envelope of the precursor ion species. Collision energy was operator adjusted to yield maximum number and intensities of the fragment ions.

Fragment ion spectra resulting from TOF/TOF analyses (300-1,000 acquisitions averaged per spectrum) were taken to search a “non-redundant” human database (‘NCBInr’; release data: Sep. 20, 2004; 106,486 entries; National Center for Biotechnology Information, Bethesda, Md.) using the MASCOT MS/MS ion search program, version 2.0.04 for Windows (Matrix Science Ltd., London, UK) with the following search parameters: mono-isotopic precursor mass tolerance of 35 ppm, fragment mass tolerance of 0.5 Da, and without a specified protease cleavage site.

Mascot ‘mowse’ scores greater than 35 were considered significant. Any identification thus obtained was verified independently by two different people, by comparing the computer-generated fragment ion series of the predicted peptide with the experimental MS/MS data. Some sequence assignments had below-threshold scores but could, nonetheless, be unequivocally assigned, as the precursor ion mass and selected fragment ion masses (b″ or y″) matched a particular peptide, representing a rung in one of the serum peptide sequence ladders.

B. Results

Peptide sequence assignment: 46 of the 68 previously selected peptides (FIGS. 2B and 17) were positively identified by MALDI-TOF/TOF MS/MS and MALDI-Q/TOF MS/MS analysis and database searches (FIG. 5A, additionally showing others (including m/z=1786.86, 2021.05, 2305.20, 2627.48)). Note that the m/z values listed in FIG. 5A are mono-isotopic and therefore smaller than the corresponding average isotopic values listed in FIGS. 16 and 17. Of note, all but a few of the peptide sequences clustered into the sets of overlapping fragments, lined up within each group at either the C- or N-terminal end, and with ladder-like truncations at the opposite ends. Some sequence assignments had below-threshold scores but could, nonetheless, be unequivocally assigned, as the precursor ion mass and selected fragment ion masses (b″ or y″) matched a particular rung in one of the ladders, taking into account whether the limited CID patterns were in agreement with established rules (Kapp, E. A. et al., 2003. Anal Chem 75:6251-6264) of preferential peptide bond cleavage (e.g., Xaa-Pro or Asp/Glu-Xaa) and the putative sequence.

Furthermore, 23 additional peptides, outside the original group of 68, could also be matched to certain sequence clusters by hypothesis-driven, targeted MS/MS analysis. Fifteen of those had significant discriminant analysis adjusted p-values (<0.0002) for at least on cancer type but typically lower ion intensities (FIG. 5B). Two others (‘2553’ and ‘2021’; yellow-coded in FIGS. 5A and 5B) displayed very high but similar MS ion intensities across all cancer groups and the control, with adjusted p-values >0.04, and can therefore be regarded as quasi-internal controls. Six more peptides (pink-coded in FIGS. 5A and 5B) that fit into the clusters were randomly observed in samples of the cancer and control groups and have neither discriminant nor internal control value. It should be noted that we used an unbiased approach to identify ‘marker peptides’, in which the peptides were selected first on the basis of discriminant analysis and then sequenced. This approach, commonly referred to as ‘ion mapping’, can be taken using any type of mass spectrometric platform (Gao, J. et al., 2003. J. Proteome Res 2:643-649; Fach, E. M. et al. 2004. Mol Cell Proteomics 3:1200-1210).

Three clusters derived from naturally occurring serum peptides, fibrinopeptide A (FPA), complement C3f and bradykinin, that are themselves generated from various plasma proteins through endoproteolytic cleavage, either before (bradykinin, cleaved from H-kininogen by a kallekrein) or during (FPA, N-terminally cleaved from fibrinogen by thrombin to form fibrin; C3f, released by Factors I and H after prior conversion of C3 to C3b) serum preparation (Jandl, J. H. 1996. Blood: Textbook of hematology. New York, N.Y.: Little, Brown and Co.; Sahu, A., and Lambris, J. D. 2001. Immunol Rev 180:35-48).

The full-length ‘founder’ peptides end with Arg, preceded by a hydrophobic amino acid (Val, Leu or Phe). Arg is partially removed from C3f and bradykinin (to form desArg-bradykinin). Similar ‘trypsin-like’ cleavages (Arg/Lys-Xaa) underlie formation of all other peptide clusters as well (see below). The C-terminal basic amino acid is preceded by a hydrophobic amino acid (F, L, V, I, W, A) in 21 and by S, Q or N in 15 out of the 39 observed cleavage sites (FIG. 15). Arg/Lys is typically removed (fully or in part) by a carboxypeptidase, except when preceded by Pro (3 out of 3 cases) or sometimes when preceded by Val (2 out 4). Further exoprotease degradation then proceeds at the N-terminal or C-terminal ends, either to completion or until it stalls; many or all of the ‘intermediates’ are typically represented (FIGS. 5A and 14). Of note, full-length C3f (m/z=2021.05) was found to be present at equally high concentrations in all patient and control sera (see B), and therefore represents a virtual internal standard.

Diagnostic MALDI-TOF spectral patterns consisting of N-terminal FPA and C3f truncations have previously been found in sera of myocardial infarction patients (Marshall, J. et al., 2003. J. Proteome Res 2:361-372). In contrast, nearly all of these peptides (19 total) were detected in control sera (FIG. 3B), and their presence was shown to be either consistently lower (all FPA fragments in all cancers; three C3f fragments in breast cancer) and/or higher (several Cf3 fragments in bladder and prostate cancer; one FPA fragment in breast cancer) in patient sera (FIG. 5A). Full-length C3f was present in all samples at equally high concentrations. Full-length FPA was virtually absent in sera from bladder cancer patients; no fibrinopeptide B or fragments thereof were found in any of the samples.

Decreased levels of FPA (fragments) in prostate, bladder and breast cancer patients, as shown here, also contrast with earlier findings indicating elevated levels of phospho-FPA in sera of ovarian cancer patients (measured by ESI-MS (Bergen, H. R., 3rd, et al., 2003. Dis Markers 19:239-249) and of FPA in gastrointestinal and breast cancers (measured immunochemically (Abbasciano, V. et al., 1987. Med Oncol Tumor Pharmacother 4:75-79; Auger, M. J. et al., 1987. Haemostasis 17:336-339).

Bradykinin and desArg-bradykinin levels were higher in sera of breast cancer patients and lower in bladder cancer patients. Of note, the pro-hydroxylated forms of each peptide also followed that trend (data not shown). The bradykinin and FPA parent proteins, fibrinogen alpha and HMW-kininogen, each contributed one additional sequence cluster, located in a different section of the precursor sequence, to the cancer serum peptide barcodes (FIGS. 5A and 6; FIGS. 14 and 15). Interestingly, the bradykinin and ‘other’ kininogen-derived peptides have very different marker properties. For example, whereas bradykinin and desArg-bradykinin were generally of lower ion intensity in bladder cancer than in control sera, the other two peptides (‘1944’ and ‘2209’) actually showed higher relative intensities in bladder cancer (FIGS. 5A and 16).

One of the peptides (‘2724’, FIG. 5A) in a cluster of sequences is derived from the inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) precursor (Salier, J. P. et al., 1996. Biochem J 315 (Pt 1):1-9) and covers amino acids 662-687 (FIG. 17) and is bracketed by two kallikrein cleavage sites (Phe-Arg-Xaa). Residues 662-688 likely represent a ‘propeptide’ of unknown function (Nishimura, H. et al., 1995. FEBS Lett 357:207-211). Like bradykinin, it ends with Pro-Phe-Arg. Several longer ITIH4 precursor fragments actually span the first kallikrein cleavage site, including ‘3272’ at 658-687, that has been reported as a biomarker for early stage ovarian cancer (Zhang, Z. et al., 2004. Cancer Res 64:5882-5890). Variations in N-terminal truncation by just a few amino acids in the ITIH4 cluster were found to produce relatively selective ‘markers’ for each of the three different cancers. Median ion intensities of peptides ‘3971’ and ‘3273’, for instance, were clearly highest in bladder cancer samples, peptides ‘2358’ and ‘2184’ were highest in breast cancer, and ‘2271’ was highest in prostate cancer. Also of note, peptide ‘2115’ matches the sequence of an ITIH4 splice variant (PRO1851; FIG. 15) and appears to have strong marker capacity for each cancer type, particularly for bladder and breast (FIG. 16).

A seventh cluster of 8 sequences, 4 on either site of a single Ile-Arg-Xaa cleavage site, is derived from the complement C4a precursor (Belt, K. T. et al., 1984. Cell 36:907-914) (FIGS. 5A, 14, and 15). This C4a-cluster has the highest incidence of ion markers for breast cancer; more than any in other cluster and also more than C4a-derived bladder cancer markers (FIG. 16). Only a single ion (‘1763’) of this cluster is an ion marker for prostate cancer, and is shared in that capacity with the other two cancer types. On the other hand, all but one ion marker derived from apolipoproteins (APO) A-I, A-IV and E are bladder cancer specific, all with appreciably higher ion intensities; the exception (APO A-IV, peptide ‘1971’) is actually highly selective and statistically the most significant (p=5.5e-13) ion marker for breast cancer (FIGS. 5A and 16).

Up-regulation of clusterin, i.e., ‘APO J’, has been correlated, by immuno-histochemistry, with progression of both prostate and bladder cancer (July, L. V. et al., 2002. Prostate 50:179-188; Scaltriti, M. et al., 2004. Int J Cancer 108:23-30; Miyake, H. et al., 2002. Urology 59:150-154). The 10-amino acid clusterin fragment detected at elevated concentrations in sera of bladder and prostate cancer patients is located at the C-terminus of the beta-chain. A single cut is, therefore, sufficient to release this peptide, following separation of the clusterin beta (N-t) and alpha (C-t) chains by cleavage of a Val-Arg-Xaa bond. A 6-amino acid sub-fragment has statistically relevant marker potential for bladder cancer (FIGS. 5A and 16), which is in keeping with the trend for most other peptides from APO A-I, A-IV, and E. Two ions (‘2602’; ‘2451’), each with significantly higher median intensities in breast cancer samples than in controls, corresponded to peptides derived from, respectively, Factor XIIIa and thransthyretin (FIGS. 5A and 5B). In contrast to the aforementioned clusters, each peptide was the only fragment from the respective precursors that we observed. Peptide ‘2602’ actually represents the C-terminal 25 amino acids of the Factor XIIIa propeptide (37-residues long) (FIGS. 14 and 15). Interestingly, Factor XIII itself has been found significantly down-regulated in breast tumors compared to normal mammary tissues (Jiang, W. G. et al., 2003. Oncol Rep 10:2039-2044).

Example 4 MALDI-TOF Mass Spectral Overlays of Selected Peaks Derived from Serum Peptide Profiling of Three Groups of Cancer Patients and Healthy Controls

All spectra were obtained and aligned as described above, and subsequently displayed using the Mass Spectra Viewer (MSV) (FIGS. 3A and 3B). Overlays of mass spectra of selected peptides of known sequence (FIG. 5) that showed statistically significant differences between peak intensities in one or more of the three binary comparisons are shown in FIG. 3A. Peptide ‘2021.05’ (i.e., C3f) is shown as an example of a peptide that is present in about equal concentrations in all serum samples analyzed in this study. Overlays of mass spectra of some as yet unidentified peptides that also showed statistically significant differences between peak intensities in one or more of the three binary comparisons are shown in FIG. 3B.

Peptides from a serum sample obtained from a breast cancer patient were extracted and analyzed by MS, and the ion of choice selected for MS/MS analysis. The fragment ion spectrum shown herein was taken for a MASCOT MS/MS Ion Search of the human segment of NR database, and retrieved a peptide sequence, GLEEELQFSLGSKINVKVGGNS (SEQ ID NO:76) ([MH]⁺=2305.19; Δ=4 ppm) with a Mascot score of 38.

Taken together, a total of 69 serum peptides are listed in FIG. 5A (with matching information provided in FIG. 5B; all 79 sequenced peptides listed in FIG. 14). Of those, 61 have clear MALDI-TOF MS-ion marker potential (adjusted p<0.0002; and, in most cases, much lower) for at least one type of cancer and are color-coded in blue (prostate cancer), green (bladder cancer) or red (breast cancer). The resulting ‘barcodes’ for the three cancer types consist of 26 (prostate), 50 (bladder) and 25 (breast) ‘bars’, i.e., peptides, several in common between any two or all three. Compared to healthy control samples, median intensities of ion markers could be up or down (represented by black dots in the colored barcodes in FIG. 5A) in any particular cancer group; 16 higher and 10 lower (16+/10−) in prostate cancer, 31+/−19 in bladder cancer, and 19+/6− in breast cancer. Only three peptides in each of the up- or down-categories were shared by all cancer groups.

One peptide from the C4a- and two from the ITIH4-cluster had consistently higher ion intensities in all cancers than in healthy controls; three FPA fragments were lower in all cancers. The rest of the ion markers were either in common between 2 groups or, more often, unique to a single patient cohort (FIG. 5A). Twenty six (17+/9−) of those were unique for bladder cancer and 16 (13+/3−) for breast cancer. To be noted are the nine APO[A-I, A-IV, E, J]-peptides and three C3f-peptides exclusively of higher ion intensities in bladder cancer, and the four C4a- two bradykinin- and one transthyretin-peptides in breast cancer. All three serum peptide ions that were uniquely of lower intensity in the breast cancer cohort each derived from C3f. Interestingly, a number of ‘shared’ marker ions had, in fact, higher median intensities than the controls in one type cancer and lower in another (FIGS. 5A and 5B). For instance, one ITIH4-peptide (‘842’) and one C3f-peptide (‘1865’) had higher median ion intensities in sera from prostate cancer patients than in, respectively, bladder and breast cancer. Five peptide ions (including those corresponding to bradykinin and desArg-bradykinin) that had higher median intensities in breast cancer samples were lower in bladder cancer and had no appreciable marker value for prostate cancer.

In an attempt to find trends in what clusters might have ion marker value for a type of cancer, or to at least better visualize any global differences that might exist, we plotted the ratios of the median ion intensities were plotted, for each of the peptides in the four major clusters, between each cancer group and the healthy controls (i.e., r=case/control). The center line in the panels of FIG. 6 represents no difference (r=1); bars pointing to the left (r<1) or right (r>1) indicate, respectively, lower or higher median. Even in case of the FPA ladder where nearly all peptides in cancer sera produced ion signals of lower intensities than in controls, the actual ratios vary for each ‘rung’ and for each cancer type. Of particular note is the seemingly total absence (r=0) of full-length FPA in sera of bladder cancer patients. The three other clusters exhibit an even more pronounced ‘internal’ variability, with median intensity ratios that were mostly over, but also equal to or under 1.

Visual inspection of the 4 color-coded graphs (33×3 total data points) in FIG. 6 readily distinguishes the three cancer types. There is a trend for peptides in bladder cancer sera to exhibit relatively high ion intensities in the C3f cluster and rather variable intensities in the C4a and ITIH4 clusters, and for some peptides in the C3f-cluster to be of lower intensity and others in the C4a-cluster to be of higher intensity in breast cancer sera. Ion intensities of peptides in prostate cancer sera don't seem to follow those trends, but are selectively more pronounced in some of the smaller peptides of the ITIH4-cluster. Interestingly, there is one rung in each of the C3f-, C4a- and ITIH4-ladders (respectively the 6^(th), 5^(th) and 5^(th) rung in the corresponding panels in FIG. 6) for which median ion intensities in the control samples were virtually zero, yet much higher in all three cancer types, resulting in very high ratios for each.

Taken together, the data in FIG. 6, based in parts on statistical analysis (FIG. 5B), visual inspection of spectra overlays (FIG. 3), peptide sequencing (FIGS. 4 and 5A) and relative ion intensity analysis, now strongly indicate that the human serum peptidome holds information, in the form of barcodes consisting of a few dozen peptides each, that can distinguish three different cancers from controls as well as from each other.

Example 5 Independent Set of Prostate Cancer Serum Samples for Validation of Established ‘Peptide-Signature’ Biomarkers

It was next tested whether the identified markers would correctly predict the class of an external validation set.

Sample Groups

An initial set of 32 serum samples from patients with advanced prostate cancer (Prostate #1) were analyzed together with 33 samples from healthy controls and two additional groups of cancer patient samples (FIG. 1A). One month later, an entirely different group of 41 advanced prostate cancer patients (Prostate #2), none previously studied, was analyzed using identical methodology (FIG. 8A), and a new spreadsheet with all data from the original 106 subjects and the new validation set, was generated. The assignment of the prostate cancer samples into the training set (Prostate 1—‘PR1’) or the test set (Prostate 2—‘PR2’) was random, but preserving the same demographic/pathological parameters (e.g., age, PSA levels, Gleason score, survival time).

Peptide ions from ‘feature list #2’ (68 peptides; see FIGS. 2A and 7) and from the ‘prostate cancer barcode’ (26 sequenced peptides; blue ‘barcode’ in FIGS. 5A and 5B) were then selectively used for comparison of the control, PR1 and PR2 groups by hierarchical clustering and principal component analysis. While not a perfect fit, samples from prostate cancer sets #1 and #2 were mixed to some extent but for the most part separated from the controls. Individual comparisons of each of these 26 peptide ions between the three sample groups indicated that the intensities of 26 out of 26 were statistically different (adjusted p<0.0002; i.e. the p-value to create the barcode—FIG. 5B) between PR1 and control, 23 out of 26 between PR2 and control, and only 1 out of 26 between PR1 and PR2.

Class Prediction Analysis of the Prostate Cancer Validation

Support vector machine (SVM)-based class predictions, in either binary or multi-class formats, were carried out using all 651, or the 68 or 14 previously selected peptides. Analyses were carried out using linear kernel (as described earlier). Similar sensitivities were obtained in all three instances, namely 100% (41/41) and 97.5% (40/41) accuracy for, respectively, binary and multi-group class predictions.

Example 6 Aminoprotease Activities in Plasma

The serum peptidome is likely largely the product of resident substrates, more specifically their proteolytic breakdown products (Koomen, J. M. et al., 2005. J Proteome Res 4:972-981); findings herein), and therefore, represents a read-out of the repertoire of proteases that exist in plasma and/or become activated during clotting. With the exception of bradykinin, much higher peptide concentrations were consistently observed in serum than in plasma (FIG. 9; and data not shown). The data presented herein indicate that cancer cells contribute unique proteases, perhaps exoproteases, which result in subtle but signature alterations of the complex equation of hundreds of peptides that can be resolved from human serum.

In an effort to begin to understand the presence and roles of exoproteases, synthetic C3f was added to fresh plasma at a concentration close to that observed in serum. As shown in FIG. 9, degradation is very fast. C-terminal Arg was removed within seconds, and the N-terminal truncations occurred in 10-15 min. The resulting pattern was similar to the endogenous one observed in serum and also illustrated the disparate ion intensities for different rungs in the ladder. However, most of the C3f ladder, except its smallest rung, disappeared upon prolonged incubation (data not shown). Exoproteolytic degradation of synthetic FPA in plasma followed a similar time course, but FPB was completely degraded in just a few minutes (data not shown. The results suggest that the operative exoprotease concentrations and activities are roughly equivalent in plasma and serum, and therefore not the consequence of coagulation.

As per Example 6, above, it is indicated that a sizable part of the human serum ‘peptidome’, as detected by MALDI-TOF MS, is generated by degradation of endogenous substrates by endogenous proteases. Peptide profiling is, therefore, a form of activity-based proteomics, by using a ‘metabolomic’ read-out that is subject to variations in enzyme panels, cofactors and inhibitors. Here, proteolytic activities of the ex-vivo coagulation and complement-degradation pathways, in combination with exoproteases, have been shown to contribute to generation of not only cancer-specific, but also ‘cancer type’-specific serum peptides. The specificity derives largely from aminopeptidase panels in serum, which is consistent with previous observations (van Hensbergen, Y., et al., 2002, Clin Cancer Res 8:3747-3754; Matrisian, L. M., et al., 2003, Cancer Res 63:6105-6109; Moffatt, S., et al., 2005, Hum Gene Ther 16:57-67; Kehlen, A., et al., 2003, Cancer Res 63:8500-8506; Rocken, C., et al., 2004, Int J Oncol 24:487-495; Carl-McGrath, S, et al., 2004, Int J Oncol 25:1223-1232; Kojima, K., et al., 1987, Biochem Med Metab Biol 37:35-41; Essler, M., et al., 2002, Proc Natl Acad Sci USA 99:2252-2257; Carrera, M. P., et al., 2005, Anticancer Res 25:193-196; Pulido-Cejudo, G., et al., 2004, Biotechnol Lett 26:1335-1339; Suganuma, T., et al., 2004, Lab Invest 84:639-648; Selvakumar, P., et al., 2004, Clin Cancer Res 10:2771-2775; Ni, R. Z., et al., 2003, World J Gastroenterol 9:710-713; Sheppard, G. S., et al., 2004, Bioorg Med Chem Lett 14:865-868; Griffith, E. C., et al., 1998, Proc Natl Acad Sci USA 95:15183-15188; Pasqualini, R., et al., 2000, Cancer Res 60:722-727; Petrovic, N., et al., 2003, J Biol Chem 278:49358-49368; O'Malley, P. G., et al., 2005, Biochem J; Fair, W. R., et al., 1997, Prostate 32:140-148).

In the discovery phase of the present studies, hundreds of features were sorted through to identify several that are most predictive of outcome. Reduction in the number of key peptides to only a few that are easily recognized between samples has been shown not to adversely affect class predictions. Focused mass spectrometric quantitation of key peptides should facilitate introduction of this technology into general clinical practice.

Example 7 MALDI-TOF MS-Based Quantitative Profiling

Relative quantitation of the rungs of a C3f ladder in a pool of 50 serum samples from thyroid carcinoma patients and a pool of 50 healthy controls was carried out. Ten reference peptides (FIG. 31) were added to the raw sera (2 picomoles/50 μL), peptides extracted on magnetic beads, MALDI spectra taken and ion intensity ratios calculated for each pair, for each pool. The relative ion intensities (ratio: endogenous/REF) were consistently higher for the peptides in the ‘cancer sera’ compared to the controls (˜20% to 100% higher) (FIG. 32, panel C). These results are in agreement with the normalized ion intensity comparisons of 40 individual cancer and 40 individual control samples; presented as spectral overlays and a heat plot in FIG. 32, panels A and B.

Example 8 MALDI-TOF MS-Based Protease Assays

The degradation conditions and times were studied for C3f and FPA in serum and plasma as described above. Synthetic C3f and FPA readily degraded in control serum and plasma; C3f rapidly (within 15-30 min), FPA rather slowly (up to 4 hours). 2 picomoles [¹³C-Leu]-labeled C3f was incubated for 30 min at RT with 50-μL aliquots of serum from 20 different breast cancer patients and 20 control samples. Four rungs (m/z=942, 1212, 1563, 1865) of the endogenous C3f degradation ladder were previously found to have a lower median ion intensity in MALDI spectra taken of breast cancer sera than control sera (FIG. 34, top panel). Upon overlay of the 40 color-coded spectra (FIG. 34, bottom panel), the equivalent four rungs in the ladder resulting from degradation of exogenous [¹³C-Leu]C3f had also generally lower ion intensities in the spectra of cancer patient sera compared to the controls, thus closely matching the endogenous patterns.

A synthetic version of the longest ITIH4-derived founder peptide (FIG. 33; #7, with N-t Pro) did not degrade in serum or plasma (data not shown), indicating that it probably is not a founder but rather a stalled degradation product of a bigger peptide.

Labeled C3f was added to two pools of serum, one from 50 samples obtained from thyroid carcinoma patients, and one from age- and gender-matched healthy controls. Aliquots were retrieved at various time points, ranging from 5 min to 5 hours, and analyzed by magnetic bead processing and a MALDI read-out; in triplicate. The 10 peptide-triplets (one for each rung in the C3f ladder) were then selected for each time point and each of the triplicates, the ratios between exogenously derived peptide and reference peptide calculated and plotted (FIG. 35).

The exogenous peptide was singly labeled (¹³C-Leu), and the reference peptide doubly labeled with ¹³C/¹⁵N-Leu, hence the 14 Da mass difference from the endogenous peptide. The time course results indicate that during the first 5 or so minutes, peptide degradation (removal of the C-t Arg) kinetics are faster in the cancer sera than in the controls. Furthermore, after 1-2 hours of incubation, clear differences in relative ion intensity were observed for the two smallest peptides in the ladder between the two samples; both higher in the cancer sample, indicating that the founder peptide was either more rapidly degraded in the cancer serum or that, alternatively, it was completely degraded to single amino acids in the control serum. 

1-77. (canceled)
 78. A method of identifying cancer of the prostate in a subject comprising detecting prostate cancer in the subject by detecting an increase in the following peptides: a complement C3f peptide or a fragment thereof, clusterin, complement C4-alpha, kininogen or factor XIII peptide fragment, or any combination thereof in a plasma or serum sample obtained from the subject relative to the level of those peptides in a healthy control, thereby identifying cancer of the prostate in the subject.
 79. The method of claim 1, further comprising detecting a decrease in fibrinopeptideA peptide or a fragment thereof, or a fibrinogen-alpha peptide fragment, or any combination thereof in a biological sample obtained from the subject.
 80. The method of claim 1, further comprising measuring an increase in an ITIH4 peptide relative to the level in a control.
 81. The method of claim 1, wherein the detecting comprises analyzing the biological sample, or a preparation thereof using mass spectrometry.
 82. The method of claim 81, wherein the mass spectrometry is MALDI TOF mass spectrometry.
 83. A method of identifying cancer of the prostate in a subject comprising detecting prostate cancer in the subject by detecting a decrease in a fibrinopeptide A or a fragment thereof and a fibrinogen-alpha peptide fragment, and an increase in a complement C3f peptide or a fragment thereof, a ITIH4, clusterin, complement C4-alpha, kininogen and factor XIII peptide fragment in a plasma or serum sample obtained from the subject relative to the level of those peptides in a healthy control, thereby identifying cancer of the prostate in the subject.
 84. The method of claim 83, wherein the fibrinopeptideA peptide fragment is selected from the group consisting of DSGEGDFLAEGGGVR (SEQ ID NO. 1), SGEGDFLAEGGGVR (SEQ ID NO. 2), GEGDFLAEGGGVR (SEQ ID NO. 3), EGDFLAEGGGVR (SEQ ID NO. 4), GDFLAEGGGVR (SEQ ID NO. 5), DFLAEGGGVR (SEQ ID NO. 6) and LAEGGGVR (SEQ ID NO. 25).
 85. The method of claim 83, wherein the complement C3f peptide fragment is selected from the group consisting of, SSKITHRIHWESASLL (SEQ ID NO. 8), SKITHRIHWESASLL (SEQ ID NO. 9), KITHRIHWESASLL (SEQ ID NO. 10), THRIHWESASLL (SEQ ID NO. 11), and IHWESASLL (SEQ ID NO. 28).
 86. The method of claim 83, wherein the ITIH4 peptide fragment is selected from the group consisting of PGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 13), SRQLGLPGPPDVPDHAAYHPF (SEQ ID NO. 15), HAAYHPFR (SEQ ID NO. 34), QLGLPGPPDVPDHAAYHPFR (SEQ ID NO. 35), HAAYHPF (SEQ ID NO. 39) NVHSGSTFFKYYLQGAK1PKPEASFSPR (SEQ ID NO. 40) and NVHSAGAAGSRMNFRPGVLSS (SEQ ID NO. 41).
 87. The method of claim 83, wherein the clusterin peptide fragment is HFFFPKSRIV (SEQ ID NO. 17).
 88. The method of claim 83, wherein the complement C4-alpha peptide fragment is GLEEELQFSLGSKINVKVGGNS (SEQ ID NO. 23).
 89. The method of claim 83, wherein the fibrinogen-alpha peptide fragment is selected from the group consisting of SSSYSKQFTSSTSYNRGDSTFESKSYKMA (SEQ ID NO. 55) and SSSYSKQFTSSTSYNRGDSTFESKSYKM (SEQ ID NO. 56).
 90. The method of claim 83, wherein the kininogen peptide fragment is NLGHGHKHERDQGHGHQ (SEQ ID NO. 52).
 91. The method of claim 83, wherein the factor XIII peptide fragment is AVPPNNSNAAEDDLPTVELQGVVPR (SEQ ID NO. 53).
 92. A method of identifying cancer of the prostate in a subject comprising: detecting in a plasma or serum biological sample obtained from the subject levels of one or more peptides selected from the group consisting of: a complement C3f peptide or a fragment thereof, clusterin peptide fragment, complement C4-alpha peptide fragment, kininogen peptide fragment, and factor XIII peptide fragment; and identifying cancer of the prostate in the subject when the levels of the one or more peptides in the subject are increased relative to the level of the corresponding peptides in a healthy control.
 93. A method of identifying prostate cancer in a subject, comprising: obtaining a plasma or serum biological sample from the subject; detecting prostate cancer in the subject by detecting levels of fibrinopeptideA or a fragment thereof, a fibrinogen-alpha peptide fragment, complement C3f peptide or a fragment thereof, ITIH4 peptide fragment, clusterin peptide fragment, complement C4-alpa peptide fragment, kininogen peptide fragment, and factor XIII peptide fragment; and identifying cancer of the prostate in the subject when the levels of the fibrinopeptideA or a fragment thereof and fibrinogen-alpha peptide fragment in the subject sample are decreased relative to the level of the corresponding peptides in a healthy control, and when the levels of complement C3f peptide or fragment thereof, ITIH4, clusterin, complement C4-alpha, kininogen and factor XIII peptide fragments are increased relative to the level of the corresponding peptides in a healthy control. 